Serum microRNA array analysis identifies miR-140-3p, miR-33b-3p and miR-671-3p as potential osteoarthritis biomarkers involved in metabolic processes.

Background: MicroRNAs (miRNAs) in circulation have emerged as promising biomarkers. In this study, we aimed to identify a circulating miRNA signature for osteoarthritis (OA) patients and in combination with bioinformatics analysis to evaluate the utility of selected differentially expressed miRNAs in the serum as potential OA biomarkers. Methods: Serum samples were collected from 12 primary OA patients, and 12 healthy individuals were screened using the Agilent Human miRNA Microarray platform interrogating 2549 miRNAs. Receiver Operating Characteristic (ROC) curves were constructed to evaluate the diagnostic performance of the deregulated miRNAs. Expression levels of selected miRNAs were validated by quantitative real-time PCR (qRT-PCR) in all serum and in articular cartilage samples from OA patients (n = 12) and healthy individuals (n = 7). Bioinformatics analysis was used to investigate the involved pathways and target genes for the above miRNAs. Results: We identified 279 differentially expressed miRNAs in the serum of OA patients compared to controls. Two hundred and five miRNAs (73.5%) were upregulated and 74 (26.5%) downregulated. ROC analysis revealed that 77 miRNAs had area under the curve (AUC) > 0.8 and p < 0.05. Bioinformatics analysis in the 77 miRNAs revealed that their target genes were involved in multiple signaling pathways associated with OA, among which FoxO, mTOR, Wnt, pI3K/akt, TGF-β signaling pathways, ECM-receptor interaction, and fatty acid biosynthesis. qRT-PCR validation in seven selected out of the 77 miRNAs revealed 3 significantly downregulated miRNAs (hsa-miR-33b-3p, hsa-miR-671-3p, and hsa-miR-140-3p) in the serum of OA patients, which were in silico predicted to be enriched in pathways involved in metabolic processes. Target-gene analysis of hsa-miR-140-3p, hsa-miR-33b-3p, and hsa-miR-671-3p revealed that InsR and IGFR1 were common targets of all three miRNAs, highlighting their involvement in regulation of metabolic processes that contribute to OA pathology. Hsa-miR-140-3p and hsa-miR-671-3p expression levels were consistently downregulated in articular cartilage of OA patients compared to healthy individuals. Conclusions: A serum miRNA signature was established for the first time using high density resolution miR-arrays in OA patients. We identified a three-miRNA signature, hsa-miR-140-3p, hsa-miR-671-3p, and hsa-miR-33b-3p, in the serum of OA patients, predicted to regulate metabolic processes, which could serve as a potential biomarker for the evaluation of OA risk and progression.Peer reviewedFinal Published versio

Probe-level linear model fitting and mixture modeling results in high accuracy detection of differential gene expression

BACKGROUND: The identification of differentially expressed genes (DEGs) from Affymetrix GeneChips arrays is currently done by first computing expression levels from the low-level probe intensities, then deriving significance by comparing these expression levels between conditions. The proposed PL-LM (Probe-Level Linear Model) method implements a linear model applied on the probe-level data to directly estimate the treatment effect. A finite mixture of Gaussian components is then used to identify DEGs using the coefficients estimated by the linear model. This approach can readily be applied to experimental design with or without replication. RESULTS: On a wholly defined dataset, the PL-LM method was able to identify 75% of the differentially expressed genes within 10% of false positives. This accuracy was achieved both using the three replicates per conditions available in the dataset and using only one replicate per condition. CONCLUSION: The method achieves, on this dataset, a higher accuracy than the best set of tools identified by the authors of the dataset, and does so using only one replicate per condition

Effect of various normalization methods on Applied Biosystems expression array system data

BACKGROUND: DNA microarray technology provides a powerful tool for characterizing gene expression on a genome scale. While the technology has been widely used in discovery-based medical and basic biological research, its direct application in clinical practice and regulatory decision-making has been questioned. A few key issues, including the reproducibility, reliability, compatibility and standardization of microarray analysis and results, must be critically addressed before any routine usage of microarrays in clinical laboratory and regulated areas can occur. In this study we investigate some of these issues for the Applied Biosystems Human Genome Survey Microarrays. RESULTS: We analyzed the gene expression profiles of two samples: brain and universal human reference (UHR), a mixture of RNAs from 10 cancer cell lines, using the Applied Biosystems Human Genome Survey Microarrays. Five technical replicates in three different sites were performed on the same total RNA samples according to manufacturer's standard protocols. Five different methods, quantile, median, scale, VSN and cyclic loess were used to normalize AB microarray data within each site. 1,000 genes spanning a wide dynamic range in gene expression levels were selected for real-time PCR validation. Using the TaqMan(® )assays data set as the reference set, the performance of the five normalization methods was evaluated focusing on the following criteria: (1) Sensitivity and reproducibility in detection of expression; (2) Fold change correlation with real-time PCR data; (3) Sensitivity and specificity in detection of differential expression; (4) Reproducibility of differentially expressed gene lists. CONCLUSION: Our results showed a high level of concordance between these normalization methods. This is true, regardless of whether signal, detection, variation, fold change measurements and reproducibility were interrogated. Furthermore, we used TaqMan(® )assays as a reference, to generate TPR and FDR plots for the various normalization methods across the assay range. Little impact is observed on the TP and FP rates in detection of differentially expressed genes. Additionally, little effect was observed by the various normalization methods on the statistical approaches analyzed which indicates a certain robustness of the analysis methods currently in use in the field, particularly when used in conjunction with the Applied Biosystems Gene Expression System

Limited response of NK92 cells to Plasmodium falciparum-infected erythrocytes

<p>Abstract</p> <p>Background</p> <p>Mechanisms by which anti-malarial immune responses occur are still not fully clear. Natural killer (NK) cells are thought to play a pivotal role in innate responses against <it>Plasmodium falciparum</it>. In this study, the suitability of NK92 cells as models for the NK mechanisms involved in the immune response against malaria was investigated.</p> <p>Methods</p> <p>NK92 cells were assessed for several signs of activation and cytotoxicity due to contact to parasites and were as well examined by oligonucleotide microarrays for an insight on the impact <it>P. falciparum</it>-infected erythrocytes have on their transcriptome. To address the parasite side of such interaction, growth inhibition assays were performed including non-NK cells as controls.</p> <p>Results</p> <p>By performing microarrays with NK92 cells, the impact of parasites on a transcriptional level was observed. The findings show that, although not evidently activated by iRBCs, NK92 cells show transcriptional signs of priming and proliferation. In addition, decreased parasitaemia was observed due to co-incubation with NK92 cells. However, such effect might not be NK-specific since irrelevant cells also affected parasite growth <it>in vitro</it>.</p> <p>Conclusions</p> <p>Although NK92 cells are here shown to behave as poor models for the NK immune response against parasites, the results obtained in this study may be of use for future investigations regarding host-parasites interactions in malaria.</p

Microbial cycling of isoprene, the most abundantly produced biological volatile organic compound on Earth

Isoprene (2-methyl-1,3-butadiene), the most abundantly produced biogenic volatile organic compound (BVOC) on Earth, is highly reactive and can have diverse and often detrimental atmospheric effects, which impact on climate and health. Most isoprene is produced by terrestrial plants, but (micro)algal production is important in aquatic environments, and the relative bacterial contribution remains unknown. Soils are a sink for isoprene, and bacteria that can use isoprene as a carbon and energy source have been cultivated and also identified using cultivation-independent methods from soils, leaves and coastal/marine environments. Bacteria belonging to the Actinobacteria are most frequently isolated and identified, and Proteobacteria have also been shown to degrade isoprene. In the freshwater-sediment isolate, Rhodococcus strain AD45, initial oxidation of isoprene to 1,2-epoxy-isoprene is catalyzed by a multicomponent isoprene monooxygenase encoded by the genes isoABCDEF. The resultant epoxide is converted to a glutathione conjugate by a glutathione S-transferase encoded by isoI, and further degraded by enzymes encoded by isoGHJ. Genome sequence analysis of actinobacterial isolates belonging to the genera Rhodococcus, Mycobacterium and Gordonia has revealed that isoABCDEF and isoGHIJ are linked in an operon, either on a plasmid or the chromosome. In Rhodococcus strain AD45 both isoprene and epoxy-isoprene induce a high level of transcription of 22 contiguous genes, including isoABCDEF and isoGHIJ. Sequence analysis of the isoA gene, encoding the large subunit of the oxygenase component of isoprene monooxygenase, from isolates has facilitated the development of PCR primers that are proving valuable in investigating the ecology of uncultivated isoprene-degrading bacteria

State-of-the art data normalization methods improve NMR-based metabolomic analysis

Extracting biomedical information from large metabolomic datasets by multivariate data analysis is of considerable complexity. Common challenges include among others screening for differentially produced metabolites, estimation of fold changes, and sample classification. Prior to these analysis steps, it is important to minimize contributions from unwanted biases and experimental variance. This is the goal of data preprocessing. In this work, different data normalization methods were compared systematically employing two different datasets generated by means of nuclear magnetic resonance (NMR) spectroscopy. To this end, two different types of normalization methods were used, one aiming to remove unwanted sample-to-sample variation while the other adjusts the variance of the different metabolites by variable scaling and variance stabilization methods. The impact of all methods tested on sample classification was evaluated on urinary NMR fingerprints obtained from healthy volunteers and patients suffering from autosomal polycystic kidney disease (ADPKD). Performance in terms of screening for differentially produced metabolites was investigated on a dataset following a Latin-square design, where varied amounts of 8 different metabolites were spiked into a human urine matrix while keeping the total spike-in amount constant. In addition, specific tests were conducted to systematically investigate the influence of the different preprocessing methods on the structure of the analyzed data. In conclusion, preprocessing methods originally developed for DNA microarray analysis, in particular, Quantile and Cubic-Spline Normalization, performed best in reducing bias, accurately detecting fold changes, and classifying samples

Pharmacokinetics and transcriptional effects of the anti-salmon lice drug emamectin benzoate in Atlantic salmon (Salmo salar L.)

Background Emamectin benzoate (EB) is a dominating pharmaceutical drug used for the treatment and control of infections by sea lice (Lepeophtheirus salmonis) on Atlantic salmon (Salmo salar L). Fish with an initial mean weight of 132 g were experimentally medicated by a standard seven-day EB treatment, and the concentrations of drug in liver, muscle and skin were examined. To investigate how EB affects Atlantic salmon transcription in liver, tissues were assessed by microarray and qPCR at 7, 14 and 35 days after the initiation of medication. Results The pharmacokinetic examination revealed highest EB concentrations in all three tissues at day 14, seven days after the end of the medication period. Only modest effects were seen on the transcriptional levels in liver, with small fold-change alterations in transcription throughout the experimental period. Gene set enrichment analysis (GSEA) indicated that EB treatment induced oxidative stress at day 7 and inflammation at day 14. The qPCR examinations showed that medication by EB significantly increased the transcription of both HSP70 and glutathione-S-transferase (GST) in liver during a period of 35 days, compared to un-treated fish, possibly via activation of enzymes involved in phase II conjugation of metabolism in the liver. Conclusion This study has shown that a standard seven-day EB treatment has only a modest effect on the transcription of genes in liver of Atlantic salmon. Based on GSEA, the medication seems to have produced a temporary oxidative stress response that might have affected protein stability and folding, followed by a secondary inflammatory response.publishedVersio

The Association Between Pre-pregnancy BMI and Preterm Delivery in a Diverse Southern California Population of Working Women

Whereas preterm birth has consistently been associated with low maternal pre-pregnancy weight, the relationship with high pre-pregnancy weight has been inconsistent. We quantified the pre-pregnancy BMI—preterm delivery (PTD) relationship using traditional BMI categories (underweight, normal weight, overweight and obese) as well as continuous BMI. Eligible women participated in California’s statewide prenatal screening program, worked during pregnancy, and delivered a live singleton birth in Southern California in 2002–2003. The final analytic sample included 354 cases delivering at <37 weeks, as identified by clinical estimate of gestational age from screening records, and 710 term normal-birthweight controls. Multivariable logistic regression models using categorical BMI levels and continuous BMI were compared. In categorical analyses, PTD was significantly associated with pre-pregnancy underweight only. Nonparametric local regression revealed a V-shaped relationship between continuous BMI and PTD, with minimum risk at the high end of normal, around 24 kg/m2. The odds ratio (OR) for PTD associated with low BMI within the normal range (19 kg/m2) was 2.84 (95%CI = 1.61–5.01); ORs for higher BMI in the overweight (29 kg/m2) and obese (34 kg/m2) ranges were 1.42 (95%CI = 1.10–1.84) and 2.01 (95% CI = 1.20–3.39) respectively, relative to 24 kg/m2). BMI categories obscured the preterm delivery risk associated with low-normal, overweight, and obese BMI. We found that higher BMI up to around 24 kg/m2 is increasingly protective of preterm delivery, beyond which a higher body mass index becomes detrimental. Current NHLBI/WHO BMI categories may be inadequate for identifying women at higher risk for PTD

Generation of a reference transcriptome for evaluating rainbow trout responses to various stressors

<p>Abstract</p> <p>Background</p> <p>Fish under intensive culture conditions are exposed to a variety of acute and chronic stressors, including high rearing densities, sub-optimal water quality, and severe thermal fluctuations. Such stressors are inherent in aquaculture production and can induce physiological responses with adverse effects on traits important to producers and consumers, including those associated with growth, nutrition, reproduction, immune response, and fillet quality. Understanding and monitoring the biological mechanisms underlying stress responses will facilitate alleviating their negative effects through selective breeding and changes in management practices, resulting in improved animal welfare and production efficiency.</p> <p>Results</p> <p>Physiological responses to five treatments associated with stress were characterized by measuring plasma lysozyme activity, glucose, lactate, chloride, and cortisol concentrations, in addition to stress-associated transcripts by quantitative PCR. Results indicate that the fish had significant stressor-specific changes in their physiological conditions. Sequencing of a pooled normalized transcriptome library created from gill, brain, liver, spleen, kidney and muscle RNA of control and stressed fish produced 3,160,306 expressed sequence tags which were assembled and annotated. SNP discovery resulted in identification of ~58,000 putative single nucleotide polymorphisms including 24,479 which were predicted to fall within exons. Of these, 4907 were predicted to occupy the first position of a codon and 4110 the second, increasing the probability to impact amino acid sequence variation and potentially gene function.</p> <p>Conclusion</p> <p>We have generated and characterized a reference transcriptome for rainbow trout that represents multiple tissues responding to multiple stressors common to aquaculture production environments. This resource compliments existing public transcriptome data and will facilitate approaches aiming to evaluate gene expression associated with stress in this species.</p