High-Resolution Epitope Positioning of a Large Collection of Neutralizing and Nonneutralizing Single-Domain Antibodies on the Enzymatic and Binding Subunits of Ricin Toxin

We previously produced a heavy-chain-only antibody (Ab) VH domain (VHH)-displayed phage library from two alpacas that had been immunized with ricin toxoid and nontoxic mixtures of the enzymatic ricin toxin A subunit (RTA) and binding ricin toxin B subunit (RTB) (D. J. Vance, J. M. Tremblay, N. J. Mantis, and C. B. Shoemaker, J Biol Chem 288:36538–36547, 2013, https://doi.org/10.1074/jbc.M113.519207). Initial and subsequent screens of that library by direct enzyme-linked immunosorbent assay (ELISA) yielded more than two dozen unique RTA- and RTB-specific VHHs, including 10 whose structures were subsequently solved in complex with RTA. To generate a more complete antigenic map of ricin toxin and to define the epitopes associated with toxin-neutralizing activity, we subjected the VHH-displayed phage library to additional “pannings” on both receptor-bound ricin and antibody-captured ricin. We now report the full-length DNA sequences, binding affinities, and neutralizing activities of 68 unique VHHs: 31 against RTA, 33 against RTB, and 4 against ricin holotoxin. Epitope positioning was achieved through cross-competition ELISAs performed with a panel of monoclonal antibodies (MAbs) and verified, in some instances, with hydrogen-deuterium exchange mass spectrometry. The 68 VHHs grouped into more than 20 different competition bins. The RTA-specific VHHs with strong toxin-neutralizing activities were confined to bins that overlapped two previously identified neutralizing hot spots, termed clusters I and II. The four RTB-specific VHHs with potent toxin-neutralizing activity grouped within three adjacent bins situated at the RTA-RTB interface near cluster II. These results provide important insights into epitope interrelationships on the surface of ricin and delineate regions of vulnerability that can be exploited for the purpose of vaccine and therapeutic development

A Collection of Single-Domain Antibodies that Crowd Ricin Toxin’s Active Site

This work is licensed under a Creative Commons Attribution 4.0 International License.In this report, we used hydrogen exchange-mass spectrometry (HX-MS) to identify the epitopes recognized by 21 single-domain camelid antibodies (VHHs) directed against the ribosome-inactivating subunit (RTA) of ricin toxin, a biothreat agent of concern to military and public health authorities. The VHHs, which derive from 11 different B-cell lineages, were binned together based on competition ELISAs with IB2, a monoclonal antibody that defines a toxin-neutralizing hotspot (“cluster 3”) located in close proximity to RTA’s active site. HX-MS analysis revealed that the 21 VHHs recognized four distinct epitope subclusters (3.1–3.4). Sixteen of the 21 VHHs grouped within subcluster 3.1 and engage RTA α-helices C and G. Three VHHs grouped within subcluster 3.2, encompassing α-helices C and G, plus α-helix B. The single VHH in subcluster 3.3 engaged RTA α-helices B and G, while the epitope of the sole VHH defining subcluster 3.4 encompassed α-helices C and E, and β-strand h. Modeling these epitopes on the surface of RTA predicts that the 20 VHHs within subclusters 3.1–3.3 physically occlude RTA’s active site cleft, while the single antibody in subcluster 3.4 associates on the active site’s upper rim.National Institutes of Allergy and Infectious Diseases, National Institutes of Health (HHSN272201400021C

Molecular Characterization of a Fus3/Kss1 Type MAPK from Puccinia striiformis f. sp. tritici, PsMAPK1

Puccinia striiformis f. sp. tritici (Pst) is an obligate biotrophic fungus that causes the destructive wheat stripe rust disease worldwide. Due to the lack of reliable transformation and gene disruption method, knowledge about the function of Pst genes involved in pathogenesis is limited. Mitogen-activated protein kinase (MAPK) genes have been shown in a number of plant pathogenic fungi to play critical roles in regulating various infection processes. In the present study, we identified and characterized the first MAPK gene PsMAPK1 in Pst. Phylogenetic analysis indicated that PsMAPK1 is a YERK1 MAP kinase belonging to the Fus3/Kss1 class. Single nucleotide polymerphisms (SNPs) and insertion/deletion were detected in the coding region of PsMAPK1 among six Pst isolates. Real-time RT-PCR analyses revealed that PsMAPK1 expression was induced at early infection stages and peaked during haustorium formation. When expressed in Fusarium graminearum, PsMAPK1 partially rescued the map1 mutant in vegetative growth and pathogenicity. It also partially complemented the defects of the Magnaporthe oryzae pmk1 mutant in appressorium formation and plant infection. These results suggest that F. graminearum and M. oryzae can be used as surrogate systems for functional analysis of well-conserved Pst genes and PsMAPK1 may play a role in the regulation of plant penetration and infectious growth in Pst

Etiologic Diagnosis of Lower Respiratory Tract Bacterial Infections Using Sputum Samples and Quantitative Loop-Mediated Isothermal Amplification

Etiologic diagnoses of lower respiratory tract infections (LRTI) have been relying primarily on bacterial cultures that often fail to return useful results in time. Although DNA-based assays are more sensitive than bacterial cultures in detecting pathogens, the molecular results are often inconsistent and challenged by doubts on false positives, such as those due to system- and environment-derived contaminations. Here we report a nationwide cohort study on 2986 suspected LRTI patients across P. R. China. We compared the performance of a DNA-based assay qLAMP (quantitative Loop-mediated isothermal AMPlification) with that of standard bacterial cultures in detecting a panel of eight common respiratory bacterial pathogens from sputum samples. Our qLAMP assay detects the panel of pathogens in 1047(69.28%) patients from 1533 qualified patients at the end. We found that the bacterial titer quantified based on qLAMP is a predictor of probability that the bacterium in the sample can be detected in culture assay. The relatedness of the two assays fits a logistic regression curve. We used a piecewise linear function to define breakpoints where latent pathogen abruptly change its competitive relationship with others in the panel. These breakpoints, where pathogens start to propagate abnormally, are used as cutoffs to eliminate the influence of contaminations from normal flora. With help of the cutoffs derived from statistical analysis, we are able to identify causative pathogens in 750 (48.92%) patients from qualified patients. In conclusion, qLAMP is a reliable method in quantifying bacterial titer. Despite the fact that there are always latent bacteria contaminated in sputum samples, we can identify causative pathogens based on cutoffs derived from statistical analysis of competitive relationship

Method and Accuracy of Extracting Surface Deformation Field from SAR Image Coregistration

InSAR can extract one-dimensional deformation along the line of sight of radar. SAR image coregistration and pixel offsets can be used to extract two-dimensional deformation along the azimuth and range directions, and can be considered as the significant complement for InSAR deformation measurement. Analyzing the method of extracting deformation field with the SAR image coregistration and pixel offsets, this paper presents the error model of deformation extraction along the azimuth and range directions. The main error components for deformation extraction from the pixel offsets are addressed. The experiments of coseismic deformation field extraction and error analysis are carried out with ASAR images of Bam earthquake and PALSAR images of Yushu earthquake. The results show that the coseismic deformation extraction accuracy is significantly affected by matching window size and oversampling factor. The deformation extraction error decreases with the increase of matching window size, and deformation extraction accuracy can be slightly improved with the increase of oversampling factor. Terrain effect can cause significant pixel displacements in SAR images over high-relief areas

Synthesis of an inorganic-framework molecularly imprinted Fe-doped TiO2 composite and its selective photo-Fenton-like degradation of acid orange II

BACKGROUND: The heterogeneous photo-Fenton-like process is emerging as a promising treatment of dye-containingwastewater because of its economic feasibility and high efficiency. However, it is still required to develop photo-Fenton-like catalysts with highly selective degradation ability and reusability. RESULTS: Inorganic-framework molecularly imprinted Fe-doped TiO2 composites (MIP/Fe-TiO2) were successfully prepared by the sol-gel method, with acid orange II as the template and TiO2 as the matrix material. The photo-Fenton-like catalysts were characterized using FESEM, EDS, BET, X-ray diffraction, FT-IR spectroscopy and UV-vis diffuse reflectance spectroscopy. These methods revealed the well-crystallized anatase phase and good nanometer-level particle sizes. The adsorption property and photo-Fenton-like activity of the catalysts were also studied in single and binary systems, respectively. CONCLUSIONS: Compared with non-imprinted Fe-TiO2 composites (NIP/Fe-TiO2), MIP/Fe-TiO2 showed higher adsorption capacity and selectivity toward the template molecule. In addition, it was found that the molecular recognition ability of MIP/Fe-TiO2 provided the catalysts with rapidly selective photo-Fenton-like degradation of low target pollutant levels (20 mg L-1) in the presence of high levels of interferential pollutant sodium dodecyl benzene sulfonate (100 mg L-1). Moreover, because of the stable physicochemical properties of the inorganic framework, the new photo-Fenton-like catalysts were resistant to photochemical attack and showed favorable reusability. (c) 2017 Society of Chemical Industr

Train a One-Million-Way Instance Classifier for Unsupervised Visual Representation Learning

This paper presents a simple unsupervised visual representation learning method with a pretext task of discriminating all images in a dataset using a parametric, instance-level classifier. The overall framework is a replica of a supervised classification model, where semantic classes (e.g., dog, bird, and ship) are replaced by instance IDs. However, scaling up the classification task from thousands of semantic labels to millions of instance labels brings specific challenges including 1) the large-scale softmax computation; 2) the slow convergence due to the infrequent visiting of instance samples; and 3) the massive number of negative classes that can be noisy. This work presents several novel techniques to handle these difficulties. First, we introduce a hybrid parallel training framework to make large-scale training feasible. Second, we present a raw-feature initialization mechanism for classification weights, which we assume offers a contrastive prior for instance discrimination and can clearly speed up converge in our experiments. Finally, we propose to smooth the labels of a few hardest classes to avoid optimizing over very similar negative pairs. While being conceptually simple, our framework achieves competitive or superior performance compared to state-of-the-art unsupervised approaches, i.e., SimCLR, MoCoV2, and PIC under ImageNet linear evaluation protocol and on several downstream visual tasks, verifying that full instance classification is a strong pretraining technique for many semantic visual tasks.Comment: Accepted by AAAI202