Peakhood: individual site context extraction for CLIP-seq peak regions.

MOTIVATION: CLIP-seq is by far the most widely used method to determine transcriptome-wide binding sites of RNA-binding proteins (RBPs). The binding site locations are identified from CLIP-seq read data by tools termed peak callers. Many RBPs bind to a spliced RNA (i.e. transcript) context, but all currently available peak callers only consider and report the genomic context. To accurately model protein binding behavior, a tool is needed for the individual context assignment to CLIP-seq peak regions. RESULTS: Here we present Peakhood, the first tool that utilizes CLIP-seq peak regions identified by peak callers, in tandem with CLIP-seq read information and genomic annotations, to determine which context applies, individually for each peak region. For sites assigned to transcript context, it further determines the most likely splice variant, and merges results for any number of datasets to obtain a comprehensive collection of transcript context binding sites. AVAILABILITY AND IMPLEMENTATION: Peakhood is freely available under MIT license at: https://github.com/BackofenLab/Peakhood. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

hnRNP R and its main interactor, the noncoding RNA 7SK, coregulate the axonal transcriptome of motoneurons.

Disturbed RNA processing and subcellular transport contribute to the pathomechanisms of motoneuron diseases such as amyotrophic lateral sclerosis and spinal muscular atrophy. RNA-binding proteins are involved in these processes, but the mechanisms by which they regulate the subcellular diversity of transcriptomes, particularly in axons, are not understood. Heterogeneous nuclear ribonucleoprotein R (hnRNP R) interacts with several proteins involved in motoneuron diseases. It is located in axons of developing motoneurons, and its depletion causes defects in axon growth. Here, we used individual nucleotide-resolution cross-linking and immunoprecipitation (iCLIP) to determine the RNA interactome of hnRNP R in motoneurons. We identified ∼3,500 RNA targets, predominantly with functions in synaptic transmission and axon guidance. Among the RNA targets identified by iCLIP, the noncoding RNA 7SK was the top interactor of hnRNP R. We detected 7SK in the nucleus and also in the cytosol of motoneurons. In axons, 7SK localized in close proximity to hnRNP R, and depletion of hnRNP R reduced axonal 7SK. Furthermore, suppression of 7SK led to defective axon growth that was accompanied by axonal transcriptome alterations similar to those caused by hnRNP R depletion. Using a series of 7SK-deletion mutants, we show that the function of 7SK in axon elongation depends on its interaction with hnRNP R but not with the PTEF-B complex involved in transcriptional regulation. These results propose a role for 7SK as an essential interactor of hnRNP R to regulate its function in axon maintenance.

Improving CLIP-seq data analysis by incorporating transcript information.

BACKGROUND: Current peak callers for identifying RNA-binding protein (RBP) binding sites from CLIP-seq data take into account genomic read profiles, but they ignore the underlying transcript information, that is information regarding splicing events. So far, there are no studies available that closer observe this issue. RESULTS: Here we show that current peak callers are susceptible to false peak calling near exon borders. We quantify its extent in publicly available datasets, which turns out to be substantial. By providing a tool called CLIPcontext for automatic transcript and genomic context sequence extraction, we further demonstrate that context choice affects the performances of RBP binding site prediction tools. Moreover, we show that known motifs of exon-binding RBPs are often enriched in transcript context sites, which should enable the recovery of more authentic binding sites. Finally, we discuss possible strategies on how to integrate transcript information into future workflows. CONCLUSIONS: Our results demonstrate the importance of incorporating transcript information in CLIP-seq data analysis. Taking advantage of the underlying transcript information should therefore become an integral part of future peak calling and downstream analysis tools.

CopomuS-Ranking Compensatory Mutations to Guide RNA-RNA Interaction Verification Experiments.

In silico RNA-RNA interaction prediction is widely applied to identify putative interaction partners and to assess interaction details in base pair resolution. To verify specific interactions, in vitro evidence can be obtained via compensatory mutation experiments. Unfortunately, the selection of compensatory mutations is non-trivial and typically based on subjective ad hoc decisions. To support the decision process, we introduce our COmPensatOry MUtation Selector CopomuS. CopomuS evaluates the effects of mutations on RNA-RNA interaction formation using a set of objective criteria, and outputs a reliable ranking of compensatory mutation candidates. For RNA-RNA interaction assessment, the state-of-the-art IntaRNA prediction tool is applied. We investigate characteristics of successfully verified RNA-RNA interactions from the literature, which guided the design of CopomuS. Finally, we evaluate its performance based on experimentally validated compensatory mutations of prokaryotic sRNAs and their target mRNAs. CopomuS predictions highly agree with known results, making it a valuable tool to support the design of verification experiments for RNA-RNA interactions. It is part of the IntaRNA package and available as stand-alone webserver for ad hoc application.

MechRNA: prediction of lncRNA mechanisms from RNA-RNA and RNA-protein interactions.

Motivation: Long non-coding RNAs (lncRNAs) are defined as transcripts longer than 200 nt that do not get translated into proteins. Often these transcripts are processed (spliced, capped and polyadenylated) and some are known to have important biological functions. However, most lncRNAs have unknown or poorly understood functions. Nevertheless, because of their potential role in cancer, lncRNAs are receiving a lot of attention, and the need for computational tools to predict their possible mechanisms of action is more than ever. Fundamentally, most of the known lncRNA mechanisms involve RNA-RNA and/or RNA-protein interactions. Through accurate predictions of each kind of interaction and integration of these predictions, it is possible to elucidate potential mechanisms for a given lncRNA. Results: Here, we introduce MechRNA, a pipeline for corroborating RNA-RNA interaction prediction and protein binding prediction for identifying possible lncRNA mechanisms involving specific targets or on a transcriptome-wide scale. The first stage uses a version of IntaRNA2 with added functionality for efficient prediction of RNA-RNA interactions with very long input sequences, allowing for large-scale analysis of lncRNA interactions with little or no loss of optimality. The second stage integrates protein binding information pre-computed by GraphProt, for both the lncRNA and the target. The final stage involves inferring the most likely mechanism for each lncRNA/target pair. This is achieved by generating candidate mechanisms from the predicted interactions, the relative locations of these interactions and correlation data, followed by selection of the most likely mechanistic explanation using a combined P-value. We applied MechRNA on a number of recently identified cancer-related lncRNAs (PCAT1, PCAT29 and ARLnc1) and also on two well-studied lncRNAs (PCA3 and 7SL). This led to the identification of hundreds of high confidence potential targets for each lncRNA and corresponding mechanisms. These predictions include the known competitive mechanism of 7SL with HuR for binding on the tumor suppressor TP53, as well as mechanisms expanding what is known about PCAT1 and ARLn1 and their targets BRCA2 and AR, respectively. For PCAT1-BRCA2, the mechanism involves competitive binding with HuR, which we confirmed using HuR immunoprecipitation assays. Availability and implementation: MechRNA is available for download at https://bitbucket.org/compbio/mechrna. Supplementary information: Supplementary data are available at Bioinformatics online.

Computational analysis of CLIP-seq data.

CLIP-seq experiments are currently the most important means for determining the binding sites of RNA binding proteins on a genome-wide level. The computational analysis can be divided into three steps. In the first pre-processing stage, raw reads have to be trimmed and mapped to the genome. This step has to be specifically adapted for each CLIP-seq protocol. The next step is peak calling, which is required to remove unspecific signals and to determine bona fide protein binding sites on target RNAs. Here, both protocol-specific approaches as well as generic peak callers are available. Despite some peak callers being more widely used, each peak caller has its specific assets and drawbacks, and it might be advantageous to compare the results of several methods. Although peak calling is often the final step in many CLIP-seq publications, an important follow-up task is the determination of binding models from CLIP-seq data. This is central because CLIP-seq experiments are highly dependent on the transcriptional state of the cell in which the experiment was performed. Thus, relying solely on binding sites determined by CLIP-seq from different cells or conditions can lead to a high false negative rate. This shortcoming can, however, be circumvented by applying models that predict additional putative binding sites.

Structure and stability of an unusual zinc-binding protein from Bacteroides thetaiotaomicron.

The crystal structure of a putative protease from Bacteroides thetaiotaomicron (ppBat) suggested the presence of a zinc ion in each protomer of the dimer as well as a flavin in the dimer interface. Since the chemical identity of the flavin and the exact mode of binding remained unclear, we have determined the crystal structure of ppBat in complex with riboflavin. The obtained structure revealed that the isoalloxazine ring is sandwiched between two tryptophan residues (Trp164) from both chains and adopts two alternate orientations with the N(10)-ribityl side chain protruding from the binding site in opposite directions. In order to characterize the zinc-binding site, we generated two single variants and one double variant in which the two coordinating cysteine residues (Cys74 and Cys111) were replaced by alanine. All three variants were unable to bind zinc demonstrating that both cysteine residues are essential for binding. Moreover, the lack of zinc binding also resulted in drastically reduced thermal stability (11-15°C). A similar effect was obtained when wild-type protein was incubated with EDTA supporting the conclusion that the zinc-binding site plays an important structural role in ppBat. On the other hand, attempts to identify proteolytic activity failed suggesting that the zinc may not act as a catalytic center in ppBat. Structurally similar zinc binding motives in other proteins were also found to play a structural rather than catalytic role and hence it appears that neither the flavin nor the zinc binding sites possess a catalytic function in ppBat.

Differentiation of heroin and cocaine using dual-energy CT-an experimental study.

OBJECTIVE: To evaluate if heroin and cocaine can be distinguished using dual-energy CT. MATERIALS AND METHODS: Twenty samples of heroin and cocaine at different concentrations and standardized compression (SC) were scanned in dual-energy mode on a newest generation Dual Energy 64-row MDCT scanner. CT number, spectral graphs, and dual-energy index (DEI) were evaluated. Results were prospectively tested on six original samples from a body packer. Wilcoxon's test was used for statistical evaluation. RESULTS: Values are given as median and range. Under SC, the CT number of cocaine samples (-29.87 Hounsfield unit (HU) [-125.85; 16.16 HU]) was higher than the CT number of heroin samples (-184.37 HU [-199.81; -159.25 HU]; p < 0.01). Slope of spectral curves for cocaine was -2.36 HU/keV [-7.15; -0.67 HU/keV], and for heroin, 1.75 HU/keV [1.28; 2.5 HU/keV] (p < 0.01). DEI was 0.0352 [0.0081; 0.0528] for cocaine and significantly higher than for heroin samples (-0.0127 [-0.0097; -0.0159]; p < 0.001). While CT number was inconclusive, all six original packs were correctly classified after evaluation of the spectral curve and DEI. In contrast to the CT number, slope of the spectral curve and DEI were independent of concentration and compression. CONCLUSION: The slope of the spectral curve and the DEI from dual-energy CT data can be used to distinguish heroin and cocaine in vitro; these results are independent of compression and concentration in the measured range.

Low-dose CT in body-packers: delineation of body packs and radiation dose in a porcine model.

PURPOSE: To compare low-dose computed tomography (CT) with standard CT and conventional radiography (CR) regarding delineation of body packs and radiation dose. METHODS: Nine samples of illicit drugs including cocaine, heroin, and hashish were positioned in the rectum of a 121.5 kg pig cadaver. Each sample was scanned on a 64-row MDCT with 120 kV: one standard modulated pelvic protocol (STD), and without modulation at 80 mA (LD80), 30 mA (LD30), and 10 mA (LD10). Additionally, conventional abdominal anterior-posterior radiographs (77 kV and 106 ± 13 mA) were taken. Body pack characteristics (wrapping, content, shape) were rated independently by two radiologists and summarized to a delineation score from 0 to 9 with scores ≥6 representing sufficient delineation. Mean delineation scores were calculated for CR and CT protocols. These were additionally differentiated for readings in soft tissue (S), lung (L), user defined, variable window settings (V), and in cumulative window evaluation including all the other window settings (SLV). Effective doses were calculated (mSv). RESULTS: The CR delineation score was insufficient (3.1 ± 2.5; 2.4 ± 0.3 mSv). For CT, the SLV window setting performed best (p < 0.01). Its score significantly (p < 0.01) declined with decreasing effective radiation doses: STD (8.8 ± 0.5; 10.6 mSv), LD80 (8.2 ± 0.7; 2.6 mSv), LD30 (6.8 ± 1.3; 1.0 mSv), and LD10 (4.6 ± 1.9; 0.3 mSv). Thus, LD30 was the protocol using the lowest but sufficient dose. Moreover, for LD30 further differentiation between the particular window settings resulted in scores of 6.4 ± 1.3 (L), 6.3 ± 1.2 (V), and 3.1 ± 1.0 (S). CONCLUSIONS: With appropriate window settings, low-dose CT at 30 mA allowed for sufficient body-pack delineation below the dose of CR, which itself performed insufficient.

Lessons learned from a corporate manufacturer on driving the adoption of nature-based solutions.

Companies are increasingly focused on driving the adoption of nature-based solutions across their organizations. Yet, implementing nature-based solutions within existing regulatory frameworks poses a unique set of challenges. In this paper, we present three nature-based solution case studies from The Dow Chemical Company and The Nature Conservancy's nearly 10-year collaboration. In the first case study, we focus on the potential benefits of reforestation to support the state's air quality improvement efforts. Ultimately, federal and state authorities did not approve of the reforestation project. Following this early setback, the collaboration team developed a suite of science-based tools that could be used to better advocate for government approval for the implementation of nature-based solutions. In the second case study, we highlight how one of these tools, the Ecosystem Services Identification & Inventory Tool, was used to improve communications about the benefits of nature-based solutions with regulatory agencies. In this case, Dow ultimately received approval for the restoration of a wetland to remediate an existing ash pond. Finally, the third case study highlights how engaging the right expertise through collaboration between the private sector and conservationists can improve land management strategies. Overall, this paper emphasizes the importance of robust conservation science, tools and expertise, and thoughtful collaboration as necessary means of driving the adoption of nature-based solutions both within a company and by its regulating entities. Integr Environ Assess Manag 2022;18:74-81. © 2021 SETAC.

Analysis of factors influencing the productivity of hammer drilling - user forces, human fatigue, drilling direction, and drill bit.

In order to be able to develop a hammer drill with which the user can work as ergonomically and productively as possible, the relevant influencing factors must be known. In addition to the unknown influence of the drilling direction, there is a lack of understanding of the relations between user forces, human fatigue, and productivity. To analyze these relations, an experiment was carried out with 15 professional users. First, the influence of feed force, drilling direction, and drill bit on the rate of penetration was examined. Taking into account the rate of penetration and human fatigue, it was then investigated which of the three feed forces produces the highest productivity. Furthermore, the lateral forces applied by the participants during the drilling process were analyzed. Based on the study, it was found that the drilling direction (p < .001, r = -0.198) and the drill bit type (p < .041, r = -0.16) have a significant influence on the rate of penetration. Moreover, it was found that the rate of penetration tends to increase with higher feed forces, however, the theoretical cumulative drilling meters decrease when taking user fatigue into account. Finally, the experiment showed that the participating professionals applied lateral forces (Mdn = 16.7 N) of 13% to the feed force when working with a hammer drill. On the basis of this knowledge, investigations can be done to analyze the influences of lateral forces on the drilling process. The findings help drill and hammer drill manufacturers in testing and development processes. For craftsmen, on the other hand, the knowledge helps to perform the task as ergonomically and time-efficiently as possible.

RNAProt: an efficient and feature-rich RNA binding protein binding site predictor.

BACKGROUND: Cross-linking and immunoprecipitation followed by next-generation sequencing (CLIP-seq) is the state-of-the-art technique used to experimentally determine transcriptome-wide binding sites of RNA-binding proteins (RBPs). However, it relies on gene expression, which can be highly variable between conditions and thus cannot provide a complete picture of the RBP binding landscape. This creates a demand for computational methods to predict missing binding sites. Although there exist various methods using traditional machine learning and lately also deep learning, we encountered several problems: many of these are not well documented or maintained, making them difficult to install and use, or are not even available. In addition, there can be efficiency issues, as well as little flexibility regarding options or supported features. RESULTS: Here, we present RNAProt, an efficient and feature-rich computational RBP binding site prediction framework based on recurrent neural networks. We compare RNAProt with 1 traditional machine learning approach and 2 deep-learning methods, demonstrating its state-of-the-art predictive performance and better run time efficiency. We further show that its implemented visualizations capture known binding preferences and thus can help to understand what is learned. Since RNAProt supports various additional features (including user-defined features, which no other tool offers), we also present their influence on benchmark set performance. Finally, we show the benefits of incorporating additional features, specifically structure information, when learning the binding sites of an hairpin loop binding RBP. CONCLUSIONS: RNAProt provides a complete framework for RBP binding site predictions, from data set generation over model training to the evaluation of binding preferences and prediction. It offers state-of-the-art predictive performance, as well as superior run time efficiency, while at the same time supporting more features and input types than any other tool available so far. RNAProt is easy to install and use, comes with comprehensive documentation, and is accompanied by informative statistics and visualizations. All this makes RNAProt a valuable tool to apply in future RBP binding site research.

Galaxy CLIP-Explorer: a web server for CLIP-Seq data analysis.

BACKGROUND: Post-transcriptional regulation via RNA-binding proteins plays a fundamental role in every organism, but the regulatory mechanisms lack important understanding. Nevertheless, they can be elucidated by cross-linking immunoprecipitation in combination with high-throughput sequencing (CLIP-Seq). CLIP-Seq answers questions about the functional role of an RNA-binding protein and its targets by determining binding sites on a nucleotide level and associated sequence and structural binding patterns. In recent years the amount of CLIP-Seq data skyrocketed, urging the need for an automatic data analysis that can deal with different experimental set-ups. However, noncanonical data, new protocols, and a huge variety of tools, especially for peak calling, made it difficult to define a standard. FINDINGS: CLIP-Explorer is a flexible and reproducible data analysis pipeline for iCLIP data that supports for the first time eCLIP, FLASH, and uvCLAP data. Individual steps like peak calling can be changed to adapt to different experimental settings. We validate CLIP-Explorer on eCLIP data, finding similar or nearly identical motifs for various proteins in comparison with other databases. In addition, we detect new sequence motifs for PTBP1 and U2AF2. Finally, we optimize the peak calling with 3 different peak callers on RBFOX2 data, discuss the difficulty of the peak-calling step, and give advice for different experimental set-ups. CONCLUSION: CLIP-Explorer finally fills the demand for a flexible CLIP-Seq data analysis pipeline that is applicable to the up-to-date CLIP protocols. The article further shows the limitations of current peak-calling algorithms and the importance of a robust peak detection.

Determination of hydroxy metabolites of cocaine from hair samples and comparison with street cocaine samples.

Drugs which are commonly smoked or sniffed (e.g. cocaine), can contaminate hair through smoke or dust; therefore testing for metabolites, especially hydroxy metabolites, is highly recommended. The presence of hydroxy metabolites in street-cocaine (COC) has been discussed. To check if detection of hydroxy metabolites definitely proves ingestion, the presence of these metabolites in street COC samples has to be checked. It is expected that the more hydrophilic hydroxy metabolites of COC are incorporated into the hair-matrix to a lesser extent. For this study 576 COC positive hair samples (≥0.1ng COC/mg hair) were analysed by LC-MS/MS for benzoylecgonine (BE), norcocaine (NC), cocaethylene (CE), ortho-, meta- and para-hydroxy COC (o-, m-, p-OH-COC), meta- and para-hydroxy BE (m-, p-OH-BE), and meta- and para-hydroxy NC (m-, p-OH-NC). The results were compared with the respective metabolite/COC concentration ratios in 146 street COC samples, confiscated by the Bavarian police. Peak areas were used to estimate BE/COC, NC/COC, CE/COC and hydroxy metabolites/COC. Similar metabolic ratios were found for o-OH-COC in 88% of the samples, but for p-OH-COC and m-OH-COC only in 5.1% and 6.8%, respectively. Notably, p- and m-OH-BE as well as p- and m-OH-NC could not be identified from seized samples. We propose that area ratios exceeding the ratios of street COC more than twice or identification of OH-BE and OH-NC enable to differentiate COC consumption from contamination. Using these criteria, consumption of the drug could be proven in 92% of COC positive samples. As detection of meta- and para-hydroxy metabolites using the above mentioned criteria is a reliable tool to distinguish between ingestion and external contamination, it is recommended to implement their measurement into daily routine work.

Analysis of the androgen receptor-regulated lncRNA landscape identifies a role for ARLNC1 in prostate cancer progression.

The androgen receptor (AR) plays a critical role in the development of the normal prostate as well as prostate cancer. Using an integrative transcriptomic analysis of prostate cancer cell lines and tissues, we identified ARLNC1 (AR-regulated long noncoding RNA 1) as an important long noncoding RNA that is strongly associated with AR signaling in prostate cancer progression. Not only was ARLNC1 induced by the AR protein, but ARLNC1 stabilized the AR transcript via RNA-RNA interaction. ARLNC1 knockdown suppressed AR expression, global AR signaling and prostate cancer growth in vitro and in vivo. Taken together, these data support a role for ARLNC1 in maintaining a positive feedback loop that potentiates AR signaling during prostate cancer progression and identify ARLNC1 as a novel therapeutic target.

RIP-Seq Suggests Translational Regulation by L7Ae in Archaea.

L7Ae is a universal archaeal protein that recognizes and stabilizes kink-turn (k-turn) motifs in RNA substrates. These structural motifs are widespread in nature and are found in many functional RNA species, including ribosomal RNAs. Synthetic biology approaches utilize L7Ae/k-turn interactions to control gene expression in eukaryotes. Here, we present results of comprehensive RNA immunoprecipitation sequencing (RIP-Seq) analysis of genomically tagged L7Ae from the hyperthermophilic archaeon Sulfolobus acidocaldarius A large set of interacting noncoding RNAs was identified. In addition, several mRNAs, including the l7ae transcript, were found to contain k-turn motifs that facilitate L7Ae binding. In vivo studies showed that L7Ae autoregulates the translation of its mRNA by binding to a k-turn motif present in the 5' untranslated region (UTR). A green fluorescent protein (GFP) reporter system was established in Escherichia coli and verified conservation of L7Ae-mediated feedback regulation in Archaea Mobility shift assays confirmed binding to a k-turn in the transcript of nop5-fibrillarin, suggesting that the expression of all C/D box sRNP core proteins is regulated by L7Ae. These studies revealed that L7Ae-mediated gene regulation evolved in archaeal organisms, generating new tools for the modulation of synthetic gene circuits in bacteria.IMPORTANCE L7Ae is an essential archaeal protein that is known to structure ribosomal RNAs and small RNAs (sRNAs) by binding to their kink-turn motifs. Here, we utilized RIP-Seq methodology to achieve a first global analysis of RNA substrates for L7Ae. Several novel interactions with noncoding RNA molecules (e.g., with the universal signal recognition particle RNA) were discovered. In addition, L7Ae was found to bind to mRNAs, including its own transcript's 5' untranslated region. This feedback-loop control is conserved in most archaea and was incorporated into a reporter system that was utilized to control gene expression in bacteria. These results demonstrate that L7Ae-mediated gene regulation evolved originally in archaeal organisms. The feedback-controlled reporter gene system can easily be adapted for synthetic biology approaches that require strict gene expression control.

Mechanism of ß-actin mRNA Recognition by ZBP1.

Zipcode binding protein 1 (ZBP1) is an oncofetal RNA-binding protein that mediates the transport and local translation of ß-actin mRNA by the KH3-KH4 di-domain, which is essential for neuronal development. The high-resolution structures of KH3-KH4 with their respective target sequences show that KH4 recognizes a non-canonical GGA sequence via an enlarged and dynamic hydrophobic groove, whereas KH3 binding to a core CA sequence occurs with low specificity. A data-informed kinetic simulation of the two-step binding reaction reveals that the overall reaction is driven by the second binding event and that the moderate affinities of the individual interactions favor RNA looping. Furthermore, the concentration of ZBP1, but not of the target RNA, modulates the interaction, which explains the functional significance of enhanced ZBP1 expression during embryonic development.

Catalytic competence, structure and stability of the cancer-associated R139W variant of the human NAD(P)H:quinone oxidoreductase 1 (NQO1).

The human NAD(P)H:quinone oxidoreductase 1 (NQO1; EC1.6.99.2) is an essential enzyme in the antioxidant defence system. Furthermore, NQO1 protects tumour suppressors like p53, p33ING1b and p73 from proteasomal degradation. The activity of NQO1 is also exploited in chemotherapy for the activation of quinone-based treatments. Various single nucleotide polymorphisms are known, such as NQO1*2 and NQO1*3 yielding protein variants of NQO1 with single amino acid replacements, i.e. P187S and R139W, respectively. While the former NOQ1 variant is linked to a higher risk for specific kinds of cancer, the role, if any, of the arginine 139 to tryptophan exchange in disease development remains obscure. On the other hand, mitomycin C-resistant human colon cancer cells were shown to harbour the NQO1*3 variant resulting in substantially reduced enzymatic activity. However, the molecular cause for this decrease remains unclear. In order to resolve this issue, recombinant NQO1 R139W has been characterized biochemically and structurally. In this report, we show by X-ray crystallography and 2D-NMR spectroscopy that this variant adopts the same structure both in the crystal as well as in solution. Furthermore, the kinetic parameters obtained for the variant are similar to those reported for the wild-type protein. Similarly, thermostability of the variant was only slightly affected by the amino acid replacement. Therefore, we conclude that the previously reported effects in human cancer cells cannot be attributed to protein stability or enzyme activity. Instead, it appears that loss of exon 4 during maturation of a large fraction of pre-mRNA is the major reason of the observed lack of enzyme activity and hence reduced activation of quinone-based chemotherapeutics.

Structural and kinetic studies on RosA, the enzyme catalysing the methylation of 8-demethyl-8-amino-d-riboflavin to the antibiotic roseoflavin.

UNLABELLED: N,N-8-demethyl-8-amino-d-riboflavin dimethyltransferase (RosA) catalyses the final dimethylation of 8-demethyl-8-amino-d-riboflavin (AF) to the antibiotic roseoflavin (RoF) in Streptomyces davawensis. In the present study, we solved the X-ray structure of RosA, and determined the binding properties of substrates and products. Moreover, we used steady-state and rapid reaction kinetic studies to obtain detailed information on the reaction mechanism. The structure of RosA was found to be similar to that of previously described S-adenosylmethionine (SAM)-dependent methyltransferases, featuring two domains: a mainly α-helical 'orthogonal bundle' and a Rossmann-like domain (α/ß twisted open sheet). Bioinformatics studies and molecular modelling enabled us to predict the potential SAM and AF binding sites in RosA, suggesting that both substrates, AF and SAM, bind independently to their respective binding pocket. This finding was confirmed by kinetic experiments that demonstrated a random-order 'bi-bi' reaction mechanism. Furthermore, we determined the dissociation constants for substrates and products by either isothermal titration calorimetry or UV/Vis absorption spectroscopy, revealing that both products, RoF and S-adenosylhomocysteine (SAH), bind more tightly to RosA compared with the substrates, AF and SAM. This suggests that RosA may contribute to roseoflavin resistance in S. davawensis. The tighter binding of products is also reflected by the results of inhibition experiments, in which RoF and SAH behave as competitive inhibitors for AF and SAM, respectively. We also showed that formation of a ternary complex of RosA, RoF and SAH (or SAM) leads to drastic spectral changes that are indicative of a hydrophobic environment. DATABASE: Structural data are available in the Protein Data Bank under accession number 4D7K.