Mechanistic role of nitrate anion in TCE dechlorination by ball milled ZVI and sulfidated ZVI: Experimental investigation and theoretical analysis.

Mechanistic role of NO3- in trichloroethylene (TCE) dechlorination by ball milled, micro-scale sulfidated and unsulfidated ZVI (e.g., S-mZVIbm and mZVIbm) was explored through experiments and density functional theory (DFT) calculations. Sulfidation inhibited NO3- reduction by mZVIbm as S weakened its interaction with NO3-. mZVIbm reduced NO3- within 2 h. This just resulted in a short-term electron competition during the dechlorination process by mZVIbm and hardly affected its sluggish dechlorination kinetics (complete TCE dechlorination in 11 d). On the contrary, NO3- suppressed TCE dechlorination by S-mZVIbm. This was attributed to that inhibited NO3- reduction by S-mZVIbm (40 % reduction in 6 h) induced continuous electron competition with TCE during the time span of its dechlorination by S-mZVIbm. NO3- reduction was also observed to facilitate formation/crystallization of Fe3O4 on both ZVI particles, promoting dechlorination by mZVIbm after 4 d while not taking effect to the S-mZVIbm/TCE system, as its dechlorination time was too short for the surface of S-mZVIbm to transform. This observation has important implication on groundwater remediation by ZVI or sulfidated ZVI PRBs under a scenario of upgradient anthropogenic release of NO3-.

Effects of non-reducible dissolved solutes on reductive dechlorination of trichloroethylene by ball milled zero valent irons.

Non-reducible solution anions have been well recognized to affect reactivity of ZVI in dechlorinating chlorinated hydrocarbons. However, their effects and corresponding functional mechanisms on electron efficiency (εe) of ZVI remain unclear. In this study, mechanochemically modified microscale sulfidated and unsulfidated ZVI particles (i.e., S-mZVIbm and mZVIbm) and trichloroethylene (TCE) were used as model particles and contaminant to explore such effects. PO43- as a corrosion promoter enhanced initial dechlorination rate by both particles. However, its passivating role as a surface complex agent became significant at the later stage of dechlorination by mZVIbm, while sulfidation alleviated this effect without inhibition of dechlorination. Compared with enhancing dechlorination, PO43- promoted hydrogen evolution reaction (HER) to a higher extent, decreasing εe for both particles by 17-73 %. HCO3- negligibly affected dechlorination by both particles, while elevated HER. Thus, HCO3- [5 mM] decreased εe for S-mZVIbm and mZVIbm by 1.9 % and 22 %. Different from PO43- and HCO3-, Cl- and SO42- showed no significant effects on dechlorination, HER, and therefore εe for both particles. These results imply that even though some co-existing anions (i.e., PO43- and HCO3-) acting as corrosion promoters could improve the dechlorination by ZVIs, they would lead to decreased εe and shortened particle reactive lifetime.

Sulfidation mitigates the passivation of zero valent iron at alkaline pHs: Experimental evidences and mechanism.

Groundwater pH is one of the most important geochemical parameters in controlling the interfacial reactions of zero-valent iron (ZVI) with water and contaminants. Ball milled, microscale ZVI (mZVIbm) efficiently dechlorinated TCE at initial stage (<24 h) at pH 6-7 but got passivated at later stage due to pH rise caused by iron corrosion. At pH > 9, mZVIbm almost completely lost its reactivity. In contrast, ball milled, sulfidated microscale ZVI (S-mZVIbm) didn't experience any reactivity loss during the whole reaction stage across pH 6-10 and could efficiently dechlorinate TCE at pH 10 with a reaction rate of 0.03 h-1. Increasing pH from 6 to 9 also enhanced electron utilization efficiency from 0.95% to 5.3%, and from 3.2% to 22%, for mZVIbm and S-mZVIbm, respectively. SEM images of the reacted particles showed that the corrosion product layer on S-mZVIbm had a puffy/porous structure while that on mZVIbm was dense, which may account for the mitigated passivation of S-mZVIbm under alkaline pHs. Density functional theory calculations show that covered S atoms on the Fe(100) surface weaken the interactions of H2O molecules with Fe surfaces, which renders the sulfidated Fe surface inefficient for H2O dissociation and resistant to surface passivation. The observation from this study provides important implication that natural sulfidation of ZVI may largely contribute to the long-term (>10 years) efficiency of TCE decontamination by permeable reactive barriers with pore water pH above 9.

Expression quantitative trait Loci acting across multiple tissues are enriched in inherited risk for coronary artery disease.

BACKGROUND: Despite recent discoveries of new genetic risk factors, the majority of risk for coronary artery disease (CAD) remains elusive. As the most proximal sensor of DNA variation, RNA abundance can help identify subpopulations of genetic variants active in and across tissues mediating CAD risk through gene expression. METHODS AND RESULTS: By generating new genomic data on DNA and RNA samples from the Stockholm Atherosclerosis Gene Expression (STAGE) study, 8156 cis-acting expression quantitative trait loci (eQTLs) for 6450 genes across 7 CAD-relevant tissues were detected. The inherited risk enrichments of tissue-defined sets of these eQTLs were assessed using 2 independent genome-wide association data sets. eQTLs acting across increasing numbers of tissues were found increasingly enriched for CAD risk and resided at regulatory hot spots. The risk enrichment of 42 eQTLs acting across 5 to 6 tissues was particularly high (≤7.3-fold) and confirmed in the combined genome-wide association data from Coronary Artery Disease Genome Wide Replication And Meta-Analysis Consortium. Sixteen of the 42 eQTLs associated with 19 master regulatory genes and 29 downstream gene sets (n>30) were further risk enriched comparable to that of the 153 genome-wide association risk single-nucleotide polymorphisms established for CAD (8.4-fold versus 10-fold). Three gene sets, governed by the master regulators FLYWCH1, PSORSIC3, and G3BP1, segregated the STAGE patients according to extent of CAD, and small interfering RNA targeting of these master regulators affected cholesterol-ester accumulation in foam cells of the THP1 monocytic cell line. CONCLUSIONS: eQTLs acting across multiple tissues are significant carriers of inherited risk for CAD. FLYWCH1, PSORSIC3, and G3BP1 are novel master regulatory genes in CAD that may be suitable targets.

kruX: matrix-based non-parametric eQTL discovery.

BACKGROUND: The Kruskal-Wallis test is a popular non-parametric statistical test for identifying expression quantitative trait loci (eQTLs) from genome-wide data due to its robustness against variations in the underlying genetic model and expression trait distribution, but testing billions of marker-trait combinations one-by-one can become computationally prohibitive. RESULTS: We developed kruX, an algorithm implemented in Matlab, Python and R that uses matrix multiplications to simultaneously calculate the Kruskal-Wallis test statistic for several millions of marker-trait combinations at once. KruX is more than ten thousand times faster than computing associations one-by-one on a typical human dataset. We used kruX and a dataset of more than 500k SNPs and 20k expression traits measured in 102 human blood samples to compare eQTLs detected by the Kruskal-Wallis test to eQTLs detected by the parametric ANOVA and linear model methods. We found that the Kruskal-Wallis test is more robust against data outliers and heterogeneous genotype group sizes and detects a higher proportion of non-linear associations, but is more conservative for calling additive linear associations. CONCLUSION: kruX enables the use of robust non-parametric methods for massive eQTL mapping without the need for a high-performance computing infrastructure and is freely available from http://krux.googlecode.com.

Context-specific transcriptional regulatory network inference from global gene expression maps using double two-way t-tests.

MOTIVATION: Transcriptional regulatory network inference methods have been studied for years. Most of them rely on complex mathematical and algorithmic concepts, making them hard to adapt, re-implement or integrate with other methods. To address this problem, we introduce a novel method based on a minimal statistical model for observing transcriptional regulatory interactions in noisy expression data, which is conceptually simple, easy to implement and integrate in any statistical software environment and equally well performing as existing methods. RESULTS: We developed a method to infer regulatory interactions based on a model where transcription factors (TFs) and their targets are both differentially expressed in a gene-specific, critical sample contrast, as measured by repeated two-way t-tests. Benchmarking on standard Escherichia coli and yeast reference datasets showed that this method performs equally well as the best existing methods. Analysis of the predicted interactions suggested that it works best to infer context-specific TF-target interactions which only co-express locally. We confirmed this hypothesis on a dataset of >1000 normal human tissue samples, where we found that our method predicts highly tissue-specific and functionally relevant interactions, whereas a global co-expression method only associates general TFs to non-specific biological processes. AVAILABILITY: A software tool called TwixTrix is available from http://twixtrix.googlecode.com. SUPPLEMENTARY INFORMATION: Supplementary Material is available from http://www.roslin.ed.ac.uk/tom-michoel/supplementary-data. CONTACT: tom.michoel@roslin.ed.ac.uk.

An integrative approach to infer regulation programs in a transcription regulatory module network.

The module network method, a special type of Bayesian network algorithms, has been proposed to infer transcription regulatory networks from gene expression data. In this method, a module represents a set of genes, which have similar expression profiles and are regulated by same transcription factors. The process of learning module networks consists of two steps: first clustering genes into modules and then inferring the regulation program (transcription factors) of each module. Many algorithms have been designed to infer the regulation program of a given gene module, and these algorithms show very different biases in detecting regulatory relationships. In this work, we explore the possibility of integrating results from different algorithms. The integration methods we select are union, intersection, and weighted rank aggregation. Experiments in a yeast dataset show that the union and weighted rank aggregation methods produce more accurate predictions than those given by individual algorithms, whereas the intersection method does not yield any improvement in the accuracy of predictions. In addition, somewhat surprisingly, the union method, which has a lower computational cost than rank aggregation, achieves comparable results as given by rank aggregation.