Papers of the Month
By Anika Dzierlenga, Mahita Kadmiel, Stephanie Smith-Roe, Salahuddin Syed, and Heather Vellers
NTP scientist helps develop predictive model of drug-induced liver injury
A National Toxicology Program (NTP) scientist was part of a team that developed an improved method for predicting drug-induced liver injury (DILI). The new method incorporates potential mechanisms of toxicity into a computer model. Accurate prediction of DILI will lead to a more streamlined drug development process and prevent compounds that cause liver injury from reaching the market.
The researchers used 17 of the Tox21 quantitative high-throughput screening assays to represent a variety of modes of action (MOA). For each MOA, they developed a quantitative structure activity relationship (QSAR) model, then combined the QSAR models for a more robust prediction of DILI. To test this approach, 333 Tox21 chemicals curated in the Food and Drug Administration’s Liver Toxicity Knowledge Base were used in a hold-out testing model.
For each of the thousand model building repetitions, 222 drugs were randomly used as a training set, and 111 drugs were used to validate the new approach compared with a standard QSAR model. This novel DILI-MOA approach drastically improved predictivity over QSAR alone. In addition, the team identified four MOA assays that were the most predictive of DILI, which involved disturbance of the following pathways — peroxisome proliferator−activated receptor gamma, thyroid receptor, estrogen receptor, and the antioxidant response element. (AD)
Citation: Wu L, Liu Z, Auerbach S, Huang R, Chen M, McEuen K, Xu J, Fang H, Tong W. 2017. Integrating drug’s mode of action into quantitative structure-activity relationships for improved prediction of drug-induced liver injury. J Chem Inf Model 57(4):1000−1006.
Mechanism of dual RNA-binding motif in Drosophila
NIEHS researchers and collaborators revealed that an RNA-binding protein called Glorund uses more than one RNA-binding mode to recognize and regulate mRNA transcripts. This study shows how a single protein can regulate diverse target RNAs.
Glorund, which is expressed in Drosophila melanogaster, or fruit flies, has little sequence similarity with mammalian proteins that are part of a family called heterogeneous nuclear ribonucleoprotein (hnRNP). However, sequences called quasi−RNA-recognition motifs (qRRMs) are well conserved between Glorund and mammalian hnRNPs. The researchers crystalized three qRRM sites found within Glorund to learn more about how they interact with RNAs.
By mutating certain regions within the qRRMs, the scientists demonstrated that Glorund not only interacts with RNA transcriptional control elements (TCEs) that contain a stem loop region enriched for uracil and adenine (UA) bases, which are structured UA-rich motifs, but they also found that Glorund also interacts with TCE regions that are enriched for guanine bases, which are single-stranded G-tracts.
Using genetically modified fruit flies, the researchers demonstrated that Glorund uses both recognition modes for translational repression of the nanos mRNA, but uses the G-tract recognition mode for regulation of other mRNAs. This work adds to the understanding of how RNAs are regulated and suggests that computational searches for regulatory regions of RNAs should include both UA-rich motifs and G-tracts. (SSR)
Citation: Tamayo JV, Teramoto T, Chatterjee S, Hall TM, Gavis ER. 2017. The Drosophila hnRNP F/H homolog Glorund uses two distinct RNA-binding modes to diversify target recognition. Cell Rep 19(1):150−161.
Threonine 522 modulates SIRT1 regulation of energy metabolism
NIEHS researchers recently revealed that modifying the phosphorylation of threonine 522 (T522) from SIRT1, a critical cellular metabolic regulator, is an important regulatory mechanism for SIRT1 activity in energy metabolism. The study provides an important link between nutritional and environmental influences on energy metabolism.
The scientists used mouse knock-in technology to replace SIRT1 T522 with glutamate- or alanine-generating TEKI (glutamate) or TAKI (alanine) mice. The TEKI mice mimicked the phosphorylation of T522, whereas the TAKI mice mimicked the dephosphorylation of T522 during a high-fat diet challenge.
By examining liver and adipose tissues, the researchers demonstrated that the TEKI allele primarily affects maturation and function of white adipose tissue. The TAKI allele disrupts systemic energy metabolism. Taken together, these findings showed that T522 phosphorylation is crucial for maintaining whole-body energy homeostasis, whereas T522 dephosphorylation is important for suppressing SIRT1 activity during adipocyte formation. (HV)
Citation: Lu J, Xu Q, Ji M, Guo X, Xu X, Fargo DC, Li X. 2017. The phosphorylation status of T522 modulates tissue-specific functions of SIRT1 in energy metabolism in mice. EMBO Rep 18(5):841−857.
ORIO: A datamining toolkit for life scientists
NIEHS scientists have created the Online Resource for Integrative Omics (ORIO), a web-based resource to investigate biological processes using next generation sequencing (NGS) data. The versatility and the accessibility of ORIO could advance the understanding of biological processes from genome-wide studies and could help bridge the gap between experimental and computational biology.
Modern technologies have made sequencing of DNA and RNA quicker and cheaper compared to a decade ago, resulting in large volumes of data. Although many web-based analysis software programs exist, most of them are designed for users with advanced computational skills. The authors made ORIO user-friendly, so life scientists without expertise in bioinformatics can analyze NGS. In addition, users can also take advantage of publicly available data sets and integrate this information with their own NGS data.
ORIO takes a systematic and comprehensive approach for providing organizational tools, analysis, and visualization of data. The authors have demonstrated the capabilities of ORIO through the analysis of a diverse set of examples, which include NGS data from ChIP-seq, DNA-seq, and RNA-seq experiments. (MK)
Citation: Lavender CA, Shapiro AJ, Burkholder AB, Bennett BD, Adelman K, Fargo DC. 2017. ORIO (Online Resource for Integrative Omics): a web-based platform for rapid integration of next generation sequencing data. Nucleic Acids Res; doi: 10.1093/nar/gkx270 [Online 11 April 2017].
Oxidative stress biomarker levels vary greatly between human conditions
Based on a meta-analysis of F2-isoprostane, a biomarker of oxidative stress, NIEHS scientists found that the amount of biomarker largely depends on the specific condition, disease, or exposure. They theorized that certain conditions had much greater differences between people affected with the condition compared with control subjects, and thus, potentially, had a much greater role for oxidative stress in their origin and development.
The authors examined 242 research journal articles that reported measured levels of 8-iso-PFG2alpha, the most often measured F2-isoprostane. These articles encompassed 50 different human health conditions. The team found that conditions characterized by inflammation, such as cystic fibrosis, chronic renal insufficiency, and preeclampsia, which is high blood pressure during pregnancy, have larger increases in 8-iso-PFG2alpha than others.
In contrast, tobacco smoking and cardiovascular disease, which have long been thought to have high levels of oxidative damage, had comparatively much lower levels of 8-iso-PFG2alpha. Because the paper provides quantitative measures of effects, a comprehensive ranking of conditions with the greatest potential for oxidative stress can be generated. This ranking may drive new research to design appropriate therapies to treat these conditions. (SS)
Citation: van’t Erve TJ, Kadiiska MB, London SJ, Mason RP. 2017. Classifying oxidative stress by F2-isoprostane levels across human diseases: a meta-analysis. Redox Biol 12:582−599.