Association between maternal micronutrient status, oxidative stress and common genetic variants in antioxidant enzymes at 15 weeks’ gestation in nulliparous women who subsequently develop pre-eclampsia

Aims: Pre-eclampsia is a pregnancy-specific condition affecting 2-7% of women and a leading cause of perinatal and maternal morbidity and mortality. Deficiencies of specific micronutrient antioxidant activities associated with copper, selenium, zinc and manganese, have previously been linked to pre-eclampsia at time of disease. Our aims were to investigate whether maternal plasma micronutrient concentrations and related antioxidant enzyme activities are altered prior to pre-eclampsia onset and to examine the dependence on genetic variations in these antioxidant enzymes. Methods: Pre-disease plasma samples (15+1 weeks’ gestation) were obtained from women enrolled in the international SCreening fOr Pregnancy Endpoints (SCOPE) study who subsequently developed pre-eclampsia (n=244), and age- and BMI-matched normotensive controls (n=472). Micronutrient concentrations were measured by inductively coupled plasma mass spectrometry; associated antioxidant enzyme activities, selenoprotein-P, caeruloplasmin concentrations and activities, antioxidant capacity and markers of oxidative stress were measured by colorimetric assays. Sixty four tagSNPs within genes encoding the antioxidant enzymes and selenoprotein-P were genotyped using allele-specific competitive PCR. Results: Plasma copper and caeruloplasmin concentrations were modestly, but significantly elevated in women who subsequently developed pre-eclampsia (both P<0.001) compared to controls (median [IQR], copper: 1957.4 [1787, 2177.5] vs. 1850.0 [1663.5, 2051.5] µg/L; caeruloplasmin: 2.5[1.4, 3.2] vs. 2.2[1.2, 3.0] µg/ml). There were no differences in other micronutrients or enzymes between groups. No relationship was observed between genotype for single nucleotide polymorphisms (SNPs) and antioxidant enzyme activity. Conclusions: This analysis of a prospective cohort study reports maternal micronutrient concentrations in combination with associated antioxidant enzymes and SNPs in their encoding genes in women at 15 weeks’ gestation that subsequently developed pre-eclampsia. The modest elevation in copper may contribute to oxidative stress, later in pregnancy, in those women that go on to develop pre-eclampsia. The lack of evidence to support the hypothesis that functional SNPs influence antioxidant enzyme activity in pregnant women argues against a role for these genes in the aetiology of pre-eclampsia

Selenoprotein gene nomenclature

The human genome contains 25 genes coding for selenocysteine-containing proteins (selenoproteins). These proteins are involved in a variety of functions, most notably redox homeostasis. Selenoprotein enzymes with known functions are designated according to these functions: TXNRD1, TXNRD2, and TXNRD3 (thioredoxin reductases), GPX1, GPX2, GPX3, GPX4 and GPX6 (glutathione peroxidases), DIO1, DIO2, and DIO3 (iodothyronine deiodinases), MSRB1 (methionine-R-sulfoxide reductase 1) and SEPHS2 (selenophosphate synthetase 2). Selenoproteins without known functions have traditionally been denoted by SEL or SEP symbols. However, these symbols are sometimes ambiguous and conflict with the approved nomenclature for several other genes. Therefore, there is a need to implement a rational and coherent nomenclature system for selenoprotein-encoding genes. Our solution is to use the root symbol SELENO followed by a letter. This nomenclature applies to SELENOF (selenoprotein F, the 15 kDa selenoprotein, SEP15), SELENOH (selenoprotein H, SELH, C11orf31), SELENOI (selenoprotein I, SELI, EPT1), SELENOK (selenoprotein K, SELK), SELENOM (selenoprotein M, SELM), SELENON (selenoprotein N, SEPN1, SELN), SELENOO (selenoprotein O, SELO), SELENOP (selenoprotein P, SeP, SEPP1, SELP), SELENOS (selenoprotein S, SELS, SEPS1, VIMP), SELENOT (selenoprotein T, SELT), SELENOV (selenoprotein V, SELV) and SELENOW (selenoprotein W, SELW, SEPW1). This system, approved by the HUGO Gene Nomenclature Committee, also resolves conflicting, missing and ambiguous designations for selenoprotein genes and is applicable to selenoproteins across vertebrates

The B6 database: a tool for the description and classification of vitamin B6-dependent enzymatic activities and of the corresponding protein families

BACKGROUND: Enzymes that depend on vitamin B6 (and in particular on its metabolically active form, pyridoxal 5'-phosphate, PLP) are of great relevance to biology and medicine, as they catalyze a wide variety of biochemical reactions mainly involving amino acid substrates. Although PLP-dependent enzymes belong to a small number of independent evolutionary lineages, they encompass more than 160 distinct catalytic functions, thus representing a striking example of divergent evolution. The importance and remarkable versatility of these enzymes, as well as the difficulties in their functional classification, create a need for an integrated source of information about them. DESCRIPTION: The B6 database http://bioinformatics.unipr.it/B6db contains documented B6-dependent activities and the relevant protein families, defined as monophyletic groups of sequences possessing the same enzymatic function. One or more families were associated to each of 121 PLP-dependent activities with known sequences. Hidden Markov models (HMMs) were built from family alignments and incorporated in the database. These HMMs can be used for the functional classification of PLP-dependent enzymes in genomic sets of predicted protein sequences. An example of such analyses (a census of human genes coding for PLP-dependent enzymes) is provided here, whereas many more are accessible through the database itself. CONCLUSION: The B6 database is a curated repository of biochemical and molecular information about an important group of enzymes. This information is logically organized and available for computational analyses, providing a key resource for the identification, classification and comparative analysis of B6-dependent enzymes

Association of Selenoprotein and Selenium Pathway Genotypes with Risk of Colorectal Cancer and Interaction with Selenium Status

Selenoprotein genetic variations and suboptimal selenium (Se) levels may contribute to the risk of colorectal cancer (CRC) development. We examined the association between CRC risk and genotype for single nucleotide polymorphisms (SNPs) in selenoprotein and Se metabolic pathway genes. Illumina Goldengate assays were designed and resulted in the genotyping of 1040 variants in 154 genes from 1420 cases and 1421 controls within the European Prospective Investigation into Cancer and Nutrition (EPIC) study. Multivariable logistic regression revealed an association of 144 individual SNPs from 63 Se pathway genes with CRC risk. However, regarding the selenoprotein genes, only TXNRD1 rs11111979 retained borderline statistical significance after adjustment for correlated tests (P-ACT = 0.10; P-ACT significance threshold was P <0.1). SNPs in Wingless/Integrated (Wnt) and Transforming growth factor (TGF) beta-signaling genes (FRZB, SMAD3, SMAD7) from pathways affected by Se intake were also associated with CRC risk after multiple testing adjustments. Interactions with Se status (using existing serum Se and Selenoprotein P data) were tested at the SNP, gene, and pathway levels. Pathway analyses using the modified Adaptive Rank Truncated Product method suggested that genes and gene x Se status interactions in antioxidant, apoptosis, and TGF-beta signaling pathways may be associated with CRC risk. This study suggests that SNPs in the Se pathway alone or in combination with suboptimal Se status may contribute to CRC development.Peer reviewe

Prediction of Drug-Target Interactions and Drug Repositioning via Network-Based Inference

Drug-target interaction (DTI) is the basis of drug discovery and design. It is time consuming and costly to determine DTI experimentally. Hence, it is necessary to develop computational methods for the prediction of potential DTI. Based on complex network theory, three supervised inference methods were developed here to predict DTI and used for drug repositioning, namely drug-based similarity inference (DBSI), target-based similarity inference (TBSI) and network-based inference (NBI). Among them, NBI performed best on four benchmark data sets. Then a drug-target network was created with NBI based on 12,483 FDA-approved and experimental drug-target binary links, and some new DTIs were further predicted. In vitro assays confirmed that five old drugs, namely montelukast, diclofenac, simvastatin, ketoconazole, and itraconazole, showed polypharmacological features on estrogen receptors or dipeptidyl peptidase-IV with half maximal inhibitory or effective concentration ranged from 0.2 to 10 µM. Moreover, simvastatin and ketoconazole showed potent antiproliferative activities on human MDA-MB-231 breast cancer cell line in MTT assays. The results indicated that these methods could be powerful tools in prediction of DTIs and drug repositioning

Determinants of selenium status in healthy adults

<p>Abstract</p> <p>Background</p> <p>Selenium (Se) status in non-deficient subjects is typically assessed by the Se contents of plasma/serum. That pool comprises two functional, specific selenoprotein components and at least one non-functional, non-specific components which respond differently to changes in Se intake. A more informative means of characterizing Se status in non-deficient individuals is needed.</p> <p>Methods</p> <p>Multiple biomarkers of Se status (plasma Se, serum selenoprotein P [SEPP1], plasma glutathione peroxidase activity [GPX3], buccal cell Se, urinary Se) were evaluated in relation to selenoprotein genotypes (GPX1, GPX3, SEPP1, SEP15), dietary Se intake, and parameters of single-carbon metabolism in a cohort of healthy, non-Se-deficient men (n = 106) and women (n = 155).</p> <p>Conclusions</p> <p>Plasma Se concentration was 142.0 ± 23.5 ng/ml, with GPX3 and serum-derived SEPP1 calculated to comprise 20% and 34%, respectively, of that total. The balance, comprised of non-specific components, accounted for virtually all of the interindividual variation in total plasma Se. Buccal cell Se was associated with age and plasma homocysteine (hCys), but not plasma Se. SEPP1 showed a quadratic relationship with body mass index, peaking at BMI 25-30. Urinary Se was greater in women than men, and was associated with metabolic body weight (kg^0.75), plasma folate, vitamin B₁₂and hCys (negatively). One <it>GPX1 </it>genotype (679T/T) was associated with significantly lower plasma Se levels than other allelic variants. Selenium intake, estimated from food frequency questionnaires, did not predict Se status as indicated by any biomarker. These results show that genotype, methyl-group status and BMI contribute to variation in Se biomarkers in Se-adequate individuals.</p

“Hot standards” for the thermoacidophilic archaeon Sulfolobus solfataricus

Within the archaea, the thermoacidophilic crenarchaeote Sulfolobus solfataricus has become an important model organism for physiology and biochemistry, comparative and functional genomics, as well as, more recently also for systems biology approaches. Within the Sulfolobus Systems Biology (“SulfoSYS”)-project the effect of changing growth temperatures on a metabolic network is investigated at the systems level by integrating genomic, transcriptomic, proteomic, metabolomic and enzymatic information for production of a silicon cell-model. The network under investigation is the central carbohydrate metabolism. The generation of high-quality quantitative data, which is critical for the investigation of biological systems and the successful integration of the different datasets, derived for example from high-throughput approaches (e.g., transcriptome or proteome analyses), requires the application and compliance of uniform standard protocols, e.g., for growth and handling of the organism as well as the “–omics” approaches. Here, we report on the establishment and implementation of standard operating procedures for the different wet-lab and in silico techniques that are applied within the SulfoSYS-project and that we believe can be useful for future projects on Sulfolobus or (hyper)thermophiles in general. Beside established techniques, it includes new methodologies like strain surveillance, the improved identification of membrane proteins and the application of crenarchaeal metabolomics

Investigating large-scale brain dynamics using field potential recordings: Analysis and interpretation

New technologies to record electrical activity from the brain on a massive scale offer tremendous opportunities for discovery. Electrical measurements of large-scale brain dynamics, termed field potentials, are especially important to understanding and treating the human brain. Here, our goal is to provide best practices on how field potential recordings (EEG, MEG, ECoG and LFP) can be analyzed to identify large-scale brain dynamics, and to highlight critical issues and limitations of interpretation in current work. We focus our discussion of analyses around the broad themes of activation, correlation, communication and coding. We provide best-practice recommendations for the analyses and interpretations using a forward model and an inverse model. The forward model describes how field potentials are generated by the activity of populations of neurons. The inverse model describes how to infer the activity of populations of neurons from field potential recordings. A recurring theme is the challenge of understanding how field potentials reflect neuronal population activity given the complexity of the underlying brain systems