Maternal iron metabolism gene variants modify umbilical cord blood lead levels by gene-environment interaction: a birth cohort study

Background: Given the relationship between iron metabolism and lead toxicokinetics, we hypothesized that polymorphisms in iron metabolism genes might modify maternal-fetal lead transfer. The objective of this study was to determine whether maternal and/or infant transferrin (TF) and hemochromatosis (HFE) gene missense variants modify the association between maternal blood lead (MBL) and umbilical cord blood lead (UCBL). Methods: We studied 476 mother-infant pairs whose archived blood specimens were genotyped for TF P570S, HFE H63D and HFE C282Y. MBL and UCBL were collected within 12 hours of delivery. Linear regression models were used to examine the association between log-transformed MBL and UCBL, examine for confounding and collinearity, and explore gene-environment interactions. Results: The geometric mean MBL was 0.61 μg/dL (range 0.03, 3.2) and UCBL 0.42 (<0.02, 3.9). Gene variants were common with carrier frequencies ranging from 12-31%; all were in Hardy-Weinberg equilibrium. In an adjusted linear regression model, log MBL was associated with log UCBL (β = 0.92, 95% CI: 0.82, 1.03; p < 0.01) such that a 1% increase in MBL was associated with a 0.92% increase in UCBL among infants born to wild-type mothers. In infants born to C282Y variants, however, a 1% increase in MBL is predicted to increase UCBL 0.65% (βMain Effect = −0.002, 95% CI: −0.09, −0.09; p = 0.97; βInteraction = −0.27, 95% CI: −0.52, −0.01; p = 0.04), representing a 35% lower placental lead transfer among women with MBL 5 μg/dL. Conclusions: Maternal HFE C282Y gene variant status is associated with greater reductions in placental transfer of lead as MBL increases. The inclusion of gene-environment interaction in risk assessment models may improve efforts to safeguard vulnerable populations. Electronic supplementary material The online version of this article (doi:10.1186/1476-069X-13-77) contains supplementary material, which is available to authorized users

Targeted knock-down of miR21 primary transcripts using snoMEN vectors induces apoptosis in human cancer cell lines

We have previously reported an antisense technology, 'snoMEN vectors', for targeted knock-down of protein coding mRNAs using human snoRNAs manipulated to contain short regions of sequence complementarity with the mRNA target. Here we characterise the use of snoMEN vectors to target the knock-down of micro RNA primary transcripts. We document the specific knock-down of miR21 in HeLa cells using plasmid vectors expressing miR21-targeted snoMEN RNAs and show this induces apoptosis. Knock-down is dependent on the presence of complementary sequences in the snoMEN vector and the induction of apoptosis can be suppressed by over-expression of miR21. Furthermore, we have also developed lentiviral vectors for delivery of snoMEN RNAs and show this increases the efficiency of vector transduction in many human cell lines that are difficult to transfect with plasmid vectors. Transduction of lentiviral vectors expressing snoMEN targeted to pri-miR21 induces apoptosis in human lung adenocarcinoma cells, which express high levels of miR21, but not in human primary cells. We show that snoMEN-mediated suppression of miRNA expression is prevented by siRNA knock-down of Ago2, but not by knock-down of Ago1 or Upf1. snoMEN RNAs colocalise with Ago2 in cell nuclei and nucleoli and can be co-immunoprecipitated from nuclear extracts by antibodies specific for Ago2

EMT and induction of miR-21 mediate metastasis development in Trp53-deficient tumours

Missense mutations in TP53 gene promote metastasis in human tumours. However, little is known about the complete loss of function of p53 in tumour metastasis. Here we show that squamous cell carcinomas generated by the specific ablation of Trp53 gene in mouse epidermis are highly metastatic. Biochemical and genome-wide mRNA and miRNA analyses demonstrated that metastases are associated with the early induction of epithelial-mesenchymal transition (EMT) and deregulated miRNA expression in primary tumours. Increased expression of miR-21 was observed in undifferentiated, prometastatic mouse tumours and in human tumours characterized by p53 mutations and distant metastasis. The augmented expression of miR-21, mediated by active mTOR and Stat3 signalling, conferred increased invasive properties to mouse keratinocytes in vitro and in vivo, whereas blockade of miR-21 in a metastatic spindle cell line inhibits metastasis development. Collectively these data identify novel molecular mechanisms leading to metastasis in vivo originated by p53 loss in epithelia

The ATLAS Level-1 Calorimeter Trigger

The ATLAS Level-1 Calorimeter Trigger uses reduced-granularity information from all the ATLAS calorimeters to search for high transverse-energy electrons, photons, tau leptons and jets, as well as high missing and total transverse energy. The calorimeter trigger electronics has a fixed latency of about 1 microsecond, using programmable custom-built digital electronics. This paper describes the Calorimeter Trigger hardware, as installed in the ATLAS electronics cavern

Integration of Evolutionary Features for the Identification of Functionally Important Residues in Major Facilitator Superfamily Transporters

The identification of functionally important residues is an important challenge for understanding the molecular mechanisms of proteins. Membrane protein transporters operate two-state allosteric conformational changes using functionally important cooperative residues that mediate long-range communication from the substrate binding site to the translocation pathway. In this study, we identified functionally important cooperative residues of membrane protein transporters by integrating sequence conservation and co-evolutionary information. A newly derived evolutionary feature, the co-evolutionary coupling number, was introduced to measure the connectivity of co-evolving residue pairs and was integrated with the sequence conservation score. We tested this method on three Major Facilitator Superfamily (MFS) transporters, LacY, GlpT, and EmrD. MFS transporters are an important family of membrane protein transporters, which utilize diverse substrates, catalyze different modes of transport using unique combinations of functional residues, and have enough characterized functional residues to validate the performance of our method. We found that the conserved cores of evolutionarily coupled residues are involved in specific substrate recognition and translocation of MFS transporters. Furthermore, a subset of the residues forms an interaction network connecting functional sites in the protein structure. We also confirmed that our method is effective on other membrane protein transporters. Our results provide insight into the location of functional residues important for the molecular mechanisms of membrane protein transporters

Structure-Based Predictive Models for Allosteric Hot Spots

In allostery, a binding event at one site in a protein modulates the behavior of a distant site. Identifying residues that relay the signal between sites remains a challenge. We have developed predictive models using support-vector machines, a widely used machine-learning method. The training data set consisted of residues classified as either hotspots or non-hotspots based on experimental characterization of point mutations from a diverse set of allosteric proteins. Each residue had an associated set of calculated features. Two sets of features were used, one consisting of dynamical, structural, network, and informatic measures, and another of structural measures defined by Daily and Gray [1]. The resulting models performed well on an independent data set consisting of hotspots and non-hotspots from five allosteric proteins. For the independent data set, our top 10 models using Feature Set 1 recalled 68–81% of known hotspots, and among total hotspot predictions, 58–67% were actual hotspots. Hence, these models have precision P = 58–67% and recall R = 68–81%. The corresponding models for Feature Set 2 had P = 55–59% and R = 81–92%. We combined the features from each set that produced models with optimal predictive performance. The top 10 models using this hybrid feature set had R = 73–81% and P = 64–71%, the best overall performance of any of the sets of models. Our methods identified hotspots in structural regions of known allosteric significance. Moreover, our predicted hotspots form a network of contiguous residues in the interior of the structures, in agreement with previous work. In conclusion, we have developed models that discriminate between known allosteric hotspots and non-hotspots with high accuracy and sensitivity. Moreover, the pattern of predicted hotspots corresponds to known functional motifs implicated in allostery, and is consistent with previous work describing sparse networks of allosterically important residues

Modeling of miRNA and Drug Action in the EGFR Signaling Pathway

MicroRNAs have gained significant interest due to their widespread occurrence and diverse functions as regulatory molecules, which are essential for cell division, growth, development and apoptosis in eukaryotes. The epidermal growth factor receptor (EGFR) signaling pathway is one of the best investigated cellular signaling pathways regulating important cellular processes and its deregulation is associated with severe diseases, such as cancer. In this study, we introduce a systems biological model of the EGFR signaling pathway integrating validated miRNA-target information according to diverse studies, in order to demonstrate essential roles of miRNA within this pathway. The model consists of 1241 reactions and contains 241 miRNAs. We analyze the impact of 100 specific miRNA inhibitors (anit-miRNAs) on this pathway and propose that the embedded miRNA-network can help to identify new drug targets of the EGFR signaling pathway and thereby support the development of new therapeutic strategies against cancer

Integrate mechanistic evidence from new approach methodologies (NAMs) into a read-across assessment to characterise trends in shared mode of action

This read-across case study characterises thirteen, structurally similar carboxylic acids demonstrating the application of in vitro and in silico human-based new approach methods, to determine biological similarity. Based on data from in vivo animal studies, the read-across hypothesis is that all analogues are steatotic and so should be considered hazardous. Transcriptomic analysis to determine differentially expressed genes (DEGs) in hepatocytes served as first tier testing to confirm a common mode-of-action and identify differences in the potency of the analogues. An adverse outcome pathway (AOP) network for hepatic steatosis, informed the design of an in vitro testing battery, targeting AOP relevant MIEs and KEs, and Dempster-Shafer decision theory was used to systematically quantify uncertainty and to define the minimal testing scope. The case study shows that the read-across hypothesis is the critical core to designing a robust, NAM-based testing strategy. By summarising the current mechanistic understanding, an AOP enables the selection of NAMs covering MIEs, early KEs, and late KEs. Experimental coverage of the AOP in this way is vital since MIEs and early KEs alone are not confirmatory of progression to the AO. This strategy exemplifies the workflow previously published by the EUTOXRISK project driving a paradigm shift towards NAM-based NGRA.Toxicolog

A study of children’s search query formulation habits

The strategies children use for digital information search in educational settings are rarely explored. Open questions remain on such fundamental issues as to which information-seeking strategies children employ, how they construct queries, and if the strategies that are taught are effective when using modern search engines. We conducted an observation study with school children to gain insights into these questions. As a result of this study, we identified query-creation and query-reformulation strategies that children use