The Primary Folding Defect and Rescue of ΔF508 CFTR Emerge during Translation of the Mutant Domain

In the vast majority of cystic fibrosis (CF) patients, deletion of residue F508 from CFTR is the cause of disease. F508 resides in the first nucleotide binding domain (NBD1) and its absence leads to CFTR misfolding and degradation. We show here that the primary folding defect arises during synthesis, as soon as NBD1 is translated. Introduction of either the I539T or G550E suppressor mutation in NBD1 partially rescues ΔF508 CFTR to the cell surface, but only I539T repaired ΔF508 NBD1. We demonstrated rescue of folding and stability of NBD1 from full-length ΔF508 CFTR expressed in cells to isolated purified domain. The co-translational rescue of ΔF508 NBD1 misfolding in CFTR by I539T advocates this domain as the most important drug target for cystic fibrosis

A Chaperone Trap Contributes to the Onset of Cystic Fibrosis

Protein folding is the primary role of proteostasis network (PN) where chaperone interactions with client proteins determine the success or failure of the folding reaction in the cell. We now address how the Phe508 deletion in the NBD1 domain of the cystic fibrosis (CF) transmembrane conductance regulator (CFTR) protein responsible for cystic fibrosis (CF) impacts the binding of CFTR with cellular chaperones. We applied single ion reaction monitoring mass spectrometry (SRM-MS) to quantitatively characterize the stoichiometry of the heat shock proteins (Hsps) in CFTR folding intermediates in vivo and mapped the sites of interaction of the NBD1 domain of CFTR with Hsp90 in vitro. Unlike folding of WT-CFTR, we now demonstrate the presence of ΔF508-CFTR in a stalled folding intermediate in stoichiometric association with the core Hsps 40, 70 and 90, referred to as a ‘chaperone trap’. Culturing cells at 30 C resulted in correction of ΔF508-CFTR trafficking and function, restoring the sub-stoichiometric association of core Hsps observed for WT-CFTR. These results support the interpretation that ΔF508-CFTR is restricted to a chaperone-bound folding intermediate, a state that may contribute to its loss of trafficking and increased targeting for degradation. We propose that stalled folding intermediates could define a critical proteostasis pathway branch-point(s) responsible for the loss of function in misfolding diseases as observed in CF

Predicting total petroleum hydrocarbons in field soils with Vis–NIR models developed on laboratory-constructed samples

Accurate quantification of petroleum hydrocarbons (PHCs) is required for optimizing remedial efforts at oil spill sites. While evaluating total petroleum hydrocarbons (TPH) in soils is often conducted using costly and time-consuming laboratory methods, visible and near-infrared reflectance spectroscopy (Vis–NIR) has been proven to be a rapid and cost-effective field-based method for soil TPH quantification. This study investigated whether Vis–NIR models calibrated from laboratory-constructed PHC soil samples could be used to accurately estimate TPH concentration of field samples. To evaluate this, a laboratory sample set was constructed by mixing crude oil with uncontaminated soil samples, and two field sample sets (F1 and F2) were collected from three PHC-impacted sites. The Vis–NIR TPH models were calibrated with four different techniques (partial least squares regression, random forest, artificial neural network, and support vector regression), and two model improvement methods (spiking and spiking with extra weight) were compared. Results showed that laboratory-based Vis– NIR models could predict TPH in field sample set F1 with moderate accuracy (R2 \u3e .53) but failed to predict TPH in field sample set F2 (R2 \u3c .13). Both spiking and spiking with extra weight improved the prediction of TPH in both field sample sets (R2 ranged from .63 to .88, respectively); the improvement was most pronounced for F2. This study suggests that Vis–NIR models developed from laboratory-constructed PHC soil samples, spiked by a small number of field sam- ple analyses, can be used to estimate TPH concentrations more efficiently and cost effectively compared with generating site-specific calibrations

Predicting total petroleum hydrocarbons in field soils with Vis–NIR models developed on laboratory-constructed samples

Accurate quantification of petroleum hydrocarbons (PHCs) is required for opti- mizing remedial efforts at oil spill sites. While evaluating total petroleum hydro- carbons (TPH) in soils is often conducted using costly and time-consuming lab- oratory methods, visible and near-infrared reflectance spectroscopy (Vis–NIR) has been proven to be a rapid and cost-effective field-based method for soil TPH quantification. This study investigated whether Vis–NIR models calibrated from laboratory-constructed PHC soil samples could be used to accurately estimate TPH concentration of field samples. To evaluate this, a laboratory sample set was constructed by mixing crude oil with uncontaminated soil samples, and two field sample sets (F1 and F2) were collected from three PHC-impacted sites. The Vis–NIR TPH models were calibrated with four different techniques (partial least squares regression, random forest, artificial neural network, and support vector regression), and two model improvement methods (spiking and spiking with extra weight) were compared. Results showed that laboratory-based Vis– NIR models could predict TPH in field sample set F1 with moderate accuracy (R2 \u3e .53) but failed to predict TPH in field sample set F2 (R2 \u3c .13). Both spik- ing and spiking with extra weight improved the prediction of TPH in both field sample sets (R2 ranged from .63 to .88, respectively); the improvement was most pronounced for F2. This study suggests that Vis–NIR models developed from laboratory-constructed PHC soil samples, spiked by a small number of field sam- ple analyses, can be used to estimate TPH concentrations more efficiently and cost effectively compared with generating site-specific calibrations

Impact of mercury speciation on its removal from water by activated carbon and organoclay

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Application of Transfer Learning and Convolutional Neural Networks for Autonomous Oil Sheen Monitoring

Oil sheen on the water surface can indicate a source of hydrocarbon in underlying subaquatic sediments. Here, we develop and test the accuracy of an algorithm for automated real-time visual monitoring of the water surface for detecting oil sheen. This detection system is part of an automated oil sheen screening system (OS-SS) that disturbs subaquatic sediments and monitors for the formation of sheen. We first created a new near-surface oil sheen image dataset. We then used this dataset to develop an image-based Oil Sheen Prediction Neural Network (OS-Net), a classification machine learning model based on a convolutional neural network (CNN), to predict the existence of oil sheen on the water surface from images. We explored the effectiveness of different strategies of transfer learning to improve the model accuracy. The performance of OS-Net and the oil detection accuracy reached up to 99% on a test dataset. Because the OS-SS uses video to monitor for sheen, we also created a real-time video-based oil sheen prediction algorithm (VOS-Net) to deploy in the OS-SS to autonomously map the spatial distribution of sheening potential of hydrocarbon-impacted subaquatic sediments

Application of Transfer Learning and Convolutional Neural Networks for Autonomous Oil Sheen Monitoring

Oil sheen on the water surface can indicate a source of hydrocarbon in underlying subaquatic sediments. Here, we develop and test the accuracy of an algorithm for automated real-time visual monitoring of the water surface for detecting oil sheen. This detection system is part of an automated oil sheen screening system (OS-SS) that disturbs subaquatic sediments and monitors for the formation of sheen. We first created a new near-surface oil sheen image dataset. We then used this dataset to develop an image-based Oil Sheen Prediction Neural Network (OS-Net), a classification machine learning model based on a convolutional neural network (CNN), to predict the existence of oil sheen on the water surface from images. We explored the effectiveness of different strategies of transfer learning to improve the model accuracy. The performance of OS-Net and the oil detection accuracy reached up to 99% on a test dataset. Because the OS-SS uses video to monitor for sheen, we also created a real-time video-based oil sheen prediction algorithm (VOS-Net) to deploy in the OS-SS to autonomously map the spatial distribution of sheening potential of hydrocarbon-impacted subaquatic sediments

Speciation of Mercury in Selected Areas of the Petroleum Value Chain

International audiencePetroleum, natural gas, and natural gas condensate can contain low levels of mercury (Hg). The speciation of Hg can affect its behavior during processing, transport, and storage so efficient and safe management of Hg requires an understanding of its chemical form in oil, gas and byproducts. Here, X-ray absorption spectroscopy was used to determine the Hg speciation in samples of solid residues collected throughout the petroleum value chain including stabilized crude oil residues, sediments from separation tanks and condensate glycol dehydrators, distillation column pipe scale, and biosludge from wastewater treatment. In all samples except glycol dehydrators, metacinnabar (beta-HgS) was the primary form of Hg. Electron microscopy on particles from a crude sediment showed nanosized (<100 nm) particles forming larger aggregates, and confirmed the colocalization of Hg and sulfur. In sediments from glycol dehydrators, organic Hg(SR)(2) accounted for similar to 60% of the Hg, with similar to 20% present as beta-HgS and/or Hg(SR)(4) species. beta-HgS was the predominant Hg species in refinery biosludge and pipe scale samples. However, the balance of Hg species present in these samples depended on the nature of the crude oil being processed, i.e. sweet (low sulfur crudes) vs sour (higher sulfur crudes). This information on Hg speciation in the petroleum value chain will inform development of better engineering controls and management practices for Hg

Minimal and local misfolding of ΔF508 CFTR.

<p>(A) Both CFTR and ΔF508 CFTR were translated <i>in vitro</i> in the presence of ³⁵S-methionine and cysteine and semi-permeabilized HT1080 cells for 60 min. Cells containing radiolabeled CFTR proteins were washed, lysed in Triton X-100, and prepared for limited proteolysis using increasing concentrations of proteinase K. The proteolytic digests were analyzed by 12% SDS-PAGE. The conformational difference between wild-type CFTR and ΔF508 CFTR is indicated by an arrowhead. (B) Relative intensities of all protease resistant fragments from a total 5 µg/ml Proteinase K digest, as in Figure 1A, were determined by total lane quantitation (Quantity One software Biorad). The y-axis represents electrophoretic mobility in 12% SDS-PA gel and the x-axis the relative intensity of the protease resistant fragments. The horizontal lines indicate the structural differences as described in A. The horizontal line indicated with an asterisk represents yet unidentified changes in the proteolytic pattern as a result of the ΔF508 mutation. The bracket represents small proteolytic fractions detected in both mutants. (C) Wild-type and ΔF508 CFTR were synthesized as in a, were subjected to 5 µg/ml proteinase K and NBD1-originated fragments were immunoprecipitated with polyclonal antibodies directed against NBD1 (Mr Pink) or against the R-region (G449). Arrowhead marks the NBD1-related fragment.</p