Evaluation of the synergistic potential and mechanisms of action for de novo designed cationic antimicrobial peptides

Antimicrobial peptides (AMPs) have emerged as promising candidates in combating antimicrobial resistance - a growing issue in healthcare. However, to develop AMPs into effective therapeutics, a thorough analysis and extensive investigations are essential. In this study, we employed an in silico approach to design cationic AMPs de novo, followed by their experimental testing. The antibacterial potential of de novo designed cationic AMPs, along with their synergistic properties in combination with conventional antibiotics was examined. Furthermore, the effects of bacterial inoculum density and metabolic state on the antibacterial activity of AMPs were evaluated. Finally, the impact of several potent AMPs on E. coli cell envelope and genomic DNA integrity was determined. Collectively, this comprehensive analysis provides insights into the unique characteristics of cationic AMPs.

Tuberculosis Chest X-Ray Image Retrieval System Using Deep Learning Based Biomarker Predictions

The world health organization's global tuberculosis (TB) report for 2022 identifies TB, with an estimated 1.6 million, as a leading cause of death. The number of new cases has risen since 2020, particularly the number of new drug-resistant cases, estimated at 450,000 in 2021. This is concerning, as treatment of patients with drug resistant TB is complex and may not always be successful. The NIAID TB Portals program is an international consortium with a primary focus on patient centric data collection and analysis for drug resistant TB. The data includes images, their associated radiological findings, clinical records, and socioeconomic information. This work describes a TB Portals' Chest X-ray based image retrieval system which enables precision medicine. An input image is used to retrieve similar images and the associated patient specific information, thus facilitating inspection of outcomes and treatment regimens from comparable patients. Image similarity is defined using clinically relevant biomarkers: gender, age, body mass index (BMI), and the percentage of lung affected per sextant. The biomarkers are predicted using variations of the DenseNet169 convolutional neural network. A multi-task approach is used to predict gender, age and BMI incorporating transfer learning from an initial training on the NIH Clinical Center CXR dataset to the TB portals dataset. The resulting gender AUC, age and BMI mean absolute errors were 0.9854, 4.03years and 1.67 k g m 2 . For the percentage of sextant affected by lesions the mean absolute errors ranged between 7% to 12% with higher error values in the middle and upper sextants which exhibit more variability than the lower sextants. The retrieval system is currently available from https://rap.tbportals.niaid.nih.gov/find_similar_cxr.

African Centers of Excellence in Bioinformatics and Data Intensive Science: Building Capacity for Enhancing Data Intensive Infectious Diseases Research in Africa

Africa faces both a disproportionate burden of infectious diseases coupled with unmet needs in bioinformatics and data science capabilities which impacts the ability of African biomedical researchers to vigorously pursue research and partner with institutions in other countries. The African Centers of Excellence in Bioinformatics and Data Intensive Science are collaborating with African academic institutions, industry partners, the Foundation for the National Institutes of Health (FNIH) and the National Institute of Allergy and Infectious Diseases (NIAID) at the National Institutes of Health (NIH) in a public-private partnership to address these challenges through enhancing computational infrastructure, fostering the development of advanced bioinformatics and data science skills among local researchers and students and providing innovative emerging technologies for infectious diseases research.

Comparative analysis of machine learning algorithms on the microbial strain-specific AMP prediction

The evolution of drug-resistant pathogenic microbial species is a major global health concern. Naturally occurring, antimicrobial peptides (AMPs) are considered promising candidates to address antibiotic resistance problems. A variety of computational methods have been developed to accurately predict AMPs. The majority of such methods are not microbial strain specific (MSS): they can predict whether a given peptide is active against some microbe, but cannot accurately calculate whether such peptide would be active against a particular MS. Due to insufficient data on most MS, only a few MSS predictive models have been developed so far. To overcome this problem, we developed a novel approach that allows to improve MSS predictive models (MSSPM), based on properties, computed for AMP sequences and characteristics of genomes, computed for target MS. New models can perform predictions of AMPs for MS that do not have data on peptides tested on them. We tested various types of feature engineering as well as different machine learning (ML) algorithms to compare the predictive abilities of resulting models. Among the ML algorithms, Random Forest and AdaBoost performed best. By using genome characteristics as additional features, the performance for all models increased relative to models relying on AMP sequence-based properties only. Our novel MSS AMP predictor is freely accessible as part of DBAASP database resource at http://dbaasp.org/prediction/genome.

Patterns of genomic interrelatedness of publicly available samples in the TB portals database

The TB Portals program is an international collaboration for the collection and dissemination of tuberculosis data from patient cases focused on drug resistance. The central database is a patient-oriented resource containing both patient and pathogen clinical and genomic information. Herein we provide a summary of the pathogen genomic data available through the TB Portals and show one potential application by examining patterns of genomic pairwise distances. Distributions of pairwise distances highlight overall patterns of genome variability within and between Mycobacterium tuberculosis phylogenomic lineages. Closely related isolates (based on whole-genome pairwise distances and time between sample collection dates) from different countries were identified as potential evidence of international transmission of drug-resistant tuberculosis. These high-level views of genomic relatedness provide information that can stimulate hypotheses for further and more detailed research.

Differentiating between drug-sensitive and drug-resistant tuberculosis with machine learning for clinical and radiological features

Background

Tuberculosis (TB) drug resistance is a worldwide public health problem that threatens progress made in TB care and control. Early detection of drug resistance is important for disease control, with discrimination between drug-resistant TB (DR-TB) and drug-sensitive TB (DS-TB) still being an open problem. The objective of this work is to investigate the relevance of readily available clinical data and data derived from chest X-rays (CXRs) in DR-TB prediction and to investigate the possibility of applying machine learning techniques to selected clinical and radiological features for discrimination between DR-TB and DS-TB. We hypothesize that the number of sextants affected by abnormalities such as nodule, cavity, collapse and infiltrate may serve as a radiological feature for DR-TB identification, and that both clinical and radiological features are important factors for machine classification of DR-TB and DS-TB.

Methods

We use data from the NIAID TB Portals program (https://tbportals.niaid.nih.gov), 1,455 DR-TB cases and 782 DS-TB cases from 11 countries. We first select three clinical features and 26 radiological features from the dataset. Then, we perform Pearson's chi-squared test to analyze the significance of the selected clinical and radiological features. Finally, we train machine classifiers based on different features and evaluate their ability to differentiate between DR-TB and DS-TB.

Results

Pearson's chi-squared test shows that two clinical features and 23 radiological features are statistically significant regarding DR-TB vs. DS-TB. A ten-fold cross-validation using a support vector machine shows that automatic discrimination between DR-TB and DS-TB achieves an average accuracy of 72.34% and an average AUC value of 78.42%, when combing all 25 statistically significant features.

Conclusions

Our study suggests that the number of affected lung sextants can be used for predicting DR-TB, and that automatic discrimination between DR-TB and DS-TB is possible, with a combination of clinical features and radiological features providing the best performance.

Radiologist observations of computed tomography (CT) images predict treatment outcome in TB Portals, a real-world database of tuberculosis (TB) cases

The TB Portals program provides a publicly accessible repository of TB case data containing multi-modal information such as case clinical characteristics, pathogen genomics, and radiomics. The real-world resource contains over 3400 TB cases, primarily drug resistant cases, and CT images with radiologist annotations are available for many of these cases. The breadth of data collected offers a patient-centric view into the etiology of the disease including the temporal context of the available imaging information. Here, we analyze a cohort of new TB cases with available radiologist observations of CTs taken around the time of initial registration of the case into the database and with available follow up to treatment outcome of cured or died. Follow up ranged from 5 weeks to a little over 2 years consistent with the longest treatment regimens for drug resistant TB and cases were registered within the years 2008 to 2019. The radiologist observations were incorporated into machine learning pipelines to test various class balancing strategies on the performance of predictive models. The modeling results support that the radiologist observations are predictive of treatment outcome. Moreover, inferential statistical analysis identifies markers of TB disease spread as having an association with poor treatment outcome including presence of radiologist observations in both lungs, swollen lymph nodes, multiple cavities, and large cavities. While the initial results are promising, further data collection is needed to incorporate methods to mitigate potential confounding such as including additional model covariates or matching cohorts on covariates of interest (e.g. demographics, BMI, comorbidity, TB subtype, etc.). Nonetheless, the preliminary results highlight the utility of the resource for hypothesis generation and exploration of potential biomarkers of TB disease severity and support these additional data collection efforts.

DBAASP v3: database of antimicrobial/cytotoxic activity and structure of peptides as a resource for development of new therapeutics

The Database of Antimicrobial Activity and Structure of Peptides (DBAASP) is an open-access, comprehensive database containing information on amino acid sequences, chemical modifications, 3D structures, bioactivities and toxicities of peptides that possess antimicrobial properties. DBAASP is updated continuously, and at present, version 3.0 (DBAASP v3) contains >15 700 entries (8000 more than the previous version), including >14 500 monomers and nearly 400 homo- and hetero-multimers. Of the monomeric antimicrobial peptides (AMPs), >12 000 are synthetic, about 2700 are ribosomally synthesized, and about 170 are non-ribosomally synthesized. Approximately 3/4 of the entries were added after the initial release of the database in 2014 reflecting the recent sharp increase in interest in AMPs. Despite the increased interest, adoption of peptide antimicrobials in clinical practice is still limited as a consequence of several factors including side effects, problems with bioavailability and high production costs. To assist in developing and optimizing de novo peptides with desired biological activities, DBAASP offers several tools including a sophisticated multifactor analysis of relevant physicochemical properties. Furthermore, DBAASP has implemented a structure modelling pipeline that automates the setup, execution and upload of molecular dynamics (MD) simulations of database peptides. At present, >3200 peptides have been populated with MD trajectories and related analyses that are both viewable within the web browser and available for download. More than 400 DBAASP entries also have links to experimentally determined structures in the Protein Data Bank. DBAASP v3 is freely accessible at http://dbaasp.org.

The technology behind TB DEPOT: a novel public analytics platform integrating tuberculosis clinical, genomic, and radiological data for visual and statistical exploration

OBJECTIVE

Clinical research informatics tools are necessary to support comprehensive studies of infectious diseases. The National Institute of Allergy and Infectious Diseases (NIAID) developed the publicly accessible Tuberculosis Data Exploration Portal (TB DEPOT) to address the complex etiology of tuberculosis (TB).

MATERIALS AND METHODS

TB DEPOT displays deidentified patient case data and facilitates analyses across a wide range of clinical, socioeconomic, genomic, and radiological factors. The solution is built using Amazon Web Services cloud-based infrastructure, .NET Core, Angular, Highcharts, R, PLINK, and other custom-developed services. Structured patient data, pathogen genomic variants, and medical images are integrated into the solution to allow seamless filtering across data domains.

RESULTS

Researchers can use TB DEPOT to query TB patient cases, create and save patient cohorts, and execute comparative statistical analyses on demand. The tool supports user-driven data exploration and fulfills the National Institute of Health's Findable, Accessible, Interoperable, and Reusable (FAIR) principles.

DISCUSSION

TB DEPOT is the first tool of its kind in the field of TB research to integrate multidimensional data from TB patient cases. Its scalable and flexible architectural design has accommodated growth in the data, organizations, types of data, feature requests, and usage. Use of client-side technologies over server-side technologies and prioritizing maintenance have been important lessons learned. Future directions are dynamically prioritized and key functionality is shared through an application programming interface.

CONCLUSION

This paper describes the platform development methodology, resulting functionality, benefits, and technical considerations of a clinical research informatics application to support increased understanding of TB.

"METAGENOTE: a simplified web platform for metadata annotation of genomic samples and streamlined submission to NCBI's sequence read archive"

Background

The improvements in genomics methods coupled with readily accessible high-throughput sequencing have contributed to our understanding of microbial species, metagenomes, infectious diseases and more. To maximize the impact of these genomics studies, it is important that data from biological samples will become publicly available with standardized metadata. The availability of data at public archives provides the hope that greater insights could be obtained through integration with multi-omics data, reproducibility of published studies, or meta-analyses of large diverse datasets. These datasets should include a description of the host, organism, environmental source of the specimen, spatial-temporal information and other relevant metadata, but unfortunately these attributes are often missing and when present, they show inconsistencies in the use of metadata standards and ontologies.

Results

METAGENOTE (https://metagenote.niaid.nih.gov) is a web portal that greatly facilitates the annotation of samples from genomic studies and streamlines the submission process of sequencing files and metadata to the Sequence Read Archive (SRA) (Leinonen R, et al, Nucleic Acids Res, 39:D19-21, 2011) for public access. This platform offers a wide selection of packages for different types of biological and experimental studies with a special emphasis on the standardization of metadata reporting. These packages follow the guidelines from the MIxS standards developed by the Genomics Standard Consortium (GSC) and adopted by the three partners of the International Nucleotides Sequencing Database Collaboration (INSDC) (Cochrane G, et al, Nucleic Acids Res, 44:D48-50, 2016) - National Center for Biotechnology Information (NCBI), European Bioinformatics Institute (EBI) and the DNA Data Bank of Japan (DDBJ). METAGENOTE then compiles, validates and manages the submission through an easy-to-use web interface minimizing submission errors and eliminating the need for submitting sequencing files via a separate file transfer mechanism.

Conclusions

METAGENOTE is a public resource that focuses on simplifying the annotation and submission process of data with its corresponding metadata. Users of METAGENOTE will benefit from the easy to use annotation interface but most importantly will be encouraged to publish metadata following standards and ontologies that make the public data available for reuse.

Performance of Qure.ai automatic classifiers against a large annotated database of patients with diverse forms of tuberculosis

Availability of trained radiologists for fast processing of CXRs in regions burdened with tuberculosis always has been a challenge, affecting both timely diagnosis and patient monitoring. The paucity of annotated images of lungs of TB patients hampers attempts to apply data-oriented algorithms for research and clinical practices. The TB Portals Program database (TBPP, https://TBPortals.niaid.nih.gov) is a global collaboration curating a large collection of the most dangerous, hard-to-cure drug-resistant tuberculosis (DR-TB) patient cases. TBPP, with 1,179 (83%) DR-TB patient cases, is a unique collection that is well positioned as a testing ground for deep learning classifiers. As of January 2019, the TBPP database contains 1,538 CXRs, of which 346 (22.5%) are annotated by a radiologist and 104 (6.7%) by a pulmonologist–leaving 1,088 (70.7%) CXRs without annotations. The Qure.ai qXR artificial intelligence automated CXR interpretation tool, was blind-tested on the 346 radiologist-annotated CXRs from the TBPP database. Qure.ai qXR CXR predictions for cavity, nodule, pleural effusion, hilar lymphadenopathy was successfully matching human expert annotations. In addition, we tested the 12 Qure.ai classifiers to find whether they correlate with treatment success (information provided by treating physicians). Ten descriptors were found as significant: abnormal CXR (p = 0.0005), pleural effusion (p = 0.048), nodule (p = 0.0004), hilar lymphadenopathy (p = 0.0038), cavity (p = 0.0002), opacity (p = 0.0006), atelectasis (p = 0.0074), consolidation (p = 0.0004), indicator of TB disease (p = < .0001), and fibrosis (p = < .0001). We conclude that applying fully automated Qure.ai CXR analysis tool is useful for fast, accurate, uniform, large-scale CXR annotation assistance, as it performed well even for DR-TB cases that were not used for initial training. Testing artificial intelligence algorithms (encapsulating both machine learning and deep learning classifiers) on diverse data collections, such as TBPP, is critically important toward progressing to clinically adopted automatic assistants for medical data analysis.

Low-tech solutions for the COVID-19 supply chain crisis

A global effort is ongoing in the scientific community and in the maker movement, which focuses on creating devices and tinkering with them, to reverse-engineer commercial medical equipment and get it to healthcare workers. For these 'low-tech' solutions to have a real impact, it is important for them to coalesce around approved designs.

TB DEPOT (Data Exploration Portal): A multi-domain tuberculosis data analysis resource

The NIAID TB Portals Program (TBPP) established a unique and growing database repository of socioeconomic, geographic, clinical, laboratory, radiological, and genomic data from patient cases of drug-resistant tuberculosis (DR-TB). Currently, there are 2,428 total cases from nine country sites (Azerbaijan, Belarus, Moldova, Georgia, Romania, China, India, Kazakhstan, and South Africa), 1,611 (66%) of which are multidrug- or extensively-drug resistant and 1,185 (49%), 863 (36%), and 952 (39%) of which contain X-ray, computed tomography (CT) scan, and genomic data, respectively. We introduce the Data Exploration Portal (TB DEPOT, https://depot.tbportals.niaid.nih.gov) to visualize and analyze these multi-domain data. The TB DEPOT leverages the TBPP integration of clinical, socioeconomic, genomic, and imaging data into standardized formats and enables user-driven, repeatable, and reproducible analyses. It furthers the TBPP goals to provide a web-enabled analytics platform to countries with a high burden of multidrug-resistant TB (MDR-TB) but limited IT resources and inaccessible data, and enables the reusability of data, in conformity with the NIH's Findable, Accessible, Interoperable, and Reusable (FAIR) principles. TB DEPOT provides access to "analysis-ready" data and the ability to generate and test complex clinically-oriented hypotheses instantaneously with minimal statistical background and data processing skills. TB DEPOT is also promising for enhancing medical training and furnishing well annotated, hard to find, MDR-TB patient cases. TB DEPOT, as part of TBPP, further fosters collaborative research efforts to better understand drug-resistant tuberculosis and aid in the development of novel diagnostics and personalized treatment regimens.

Nephele: a cloud platform for simplified, standardized and reproducible microbiome data analysis

Motivation

Widespread interest in the study of the microbiome has resulted in data proliferation and the development of powerful computational tools. However, many scientific researchers lack the time, training, or infrastructure to work with large datasets or to install and use command line tools.

Results

The National Institute of Allergy and Infectious Diseases (NIAID) has created Nephele, a cloud-based microbiome data analysis platform with standardized pipelines and a simple web interface for transforming raw data into biological insights. Nephele integrates common microbiome analysis tools as well as valuable reference datasets like the healthy human subjects cohort of the Human Microbiome Project (HMP). Nephele is built on the Amazon Web Services cloud, which provides centralized and automated storage and compute capacity, thereby reducing the burden on researchers and their institutions.

Availability and implementation

https://nephele.niaid.nih.gov and https://github.com/niaid/Nephele.

Contact

darrell.hurt@nih.gov.

Tyrosine-sulfated V2 peptides inhibit HIV-1 infection via coreceptor mimicry

Tyrosine sulfation is a post-translational modification that facilitates protein-protein interaction. Two sulfated tyrosines (Tys173 and Tys177) were recently identified within the second variable (V2) loop of the major HIV-1 envelope glycoprotein, gp120, and shown to contribute to stabilizing the intramolecular interaction between V2 and the third variable (V3) loop. Here, we report that tyrosine-sulfated peptides derived from V2 act as structural and functional mimics of the CCR5 N-terminus and potently block HIV-1 infection. Nuclear magnetic and surface plasmon resonance analyses indicate that a tyrosine-sulfated V2 peptide (pV2α-Tys) adopts a CCR5-like helical conformation and directly interacts with gp120 in a CD4-dependent fashion, competing with a CCR5 N-terminal peptide. Sulfated V2 mimics, but not their non-sulfated counterparts, inhibit HIV-1 entry and fusion by preventing coreceptor utilization, with the highly conserved C-terminal sulfotyrosine, Tys177, playing a dominant role. Unlike CCR5 N-terminal peptides, V2 mimics inhibit a broad range of HIV-1 strains irrespective of their coreceptor tropism, highlighting the overall structural conservation of the coreceptor-binding site in gp120. These results document the use of receptor mimicry by a retrovirus to occlude a key neutralization target site and provide leads for the design of therapeutic strategies against HIV-1.

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Tyrosine-sulfated peptides derived from the V2 domain of HIV-1 gp120 mimic the N-terminal domain of the CCR5 coreceptor.

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Tyrosine-sulfated V2 peptides are potent and broad-spectrum inhibitors of HIV-1 infection.

Understanding how HIV-1 protects its outer envelope from the immune system may help devise effective strategies for treatment and vaccine. We derived synthetic peptides from the V2 loop of the external HIV-1 envelope glycoprotein, gp120, which contains sulfate-modified tyrosines that contribute to maintaining the envelope in an antibody-protected configuration. We found that these peptides mimic the structure and function of CCR5, a key cellular coreceptor for HIV-1, interacting with and occluding a major CCR5-binding site in gp120. Tyrosine-sulfated V2 peptides potently block HIV-1 entry and may serve as templates for the design of new antiviral inhibitors.

3D Printing in the Laboratory: Maximize Time and Funds with Customized and Open-Source Labware

3D printing, also known as additive manufacturing, is the computer-guided process of fabricating physical objects by depositing successive layers of material. It has transformed manufacturing across virtually every industry, bringing about incredible advances in research and medicine. The rapidly growing consumer market now includes convenient and affordable "desktop" 3D printers. These are being used in the laboratory to create custom 3D-printed equipment, and a growing community of designers are contributing open-source, cost-effective innovations that can be used by both professionals and enthusiasts. User stories from investigators at the National Institutes of Health and the biomedical research community demonstrate the power of 3D printing to save valuable time and funding. While adoption of 3D printing has been slow in the biosciences to date, the potential is vast. The market predicts that within several years, 3D printers could be commonplace within the home; with so many practical uses for 3D printing, we anticipate that the technology will also play an increasingly important role in the laboratory.

Structural Analysis on the Pathologic Mutant Glucocorticoid Receptor Ligand-Binding Domains

Glucocorticoid receptor (GR) gene mutations may cause familial or sporadic generalized glucocorticoid resistance syndrome. Most of the missense forms distribute in the ligand-binding domain and impair its ligand-binding activity and formation of the activation function (AF)-2 that binds LXXLL motif-containing coactivators. We performed molecular dynamics simulations to ligand-binding domain of pathologic GR mutants to reveal their structural defects. Several calculated parameters including interaction energy for dexamethasone or the LXXLL peptide indicate that destruction of ligand-binding pocket (LBP) is a primary character. Their LBP defects are driven primarily by loss/reduction of the electrostatic interaction formed by R611 and T739 of the receptor to dexamethasone and a subsequent conformational mismatch, which deacylcortivazol resolves with its large phenylpyrazole moiety and efficiently stimulates transcriptional activity of the mutant receptors with LBP defect. Reduced affinity of the LXXLL peptide to AF-2 is caused mainly by disruption of the electrostatic bonds to the noncore leucine residues of this peptide that determine the peptide's specificity to GR, as well as by reduced noncovalent interaction against core leucines and subsequent exposure of the AF-2 surface to solvent. The results reveal molecular defects of pathologic mutant receptors and provide important insights to the actions of wild-type GR.

DBAASP v.2: an enhanced database of structure and antimicrobial/cytotoxic activity of natural and synthetic peptides

Antimicrobial peptides (AMPs) are anti-infectives that may represent a novel and untapped class of biotherapeutics. Increasing interest in AMPs means that new peptides (natural and synthetic) are discovered faster than ever before. We describe herein a new version of the Database of Antimicrobial Activity and Structure of Peptides (DBAASPv.2, which is freely accessible at http://dbaasp.org). This iteration of the database reports chemical structures and empirically-determined activities (MICs, IC50, etc.) against more than 4200 specific target microbes for more than 2000 ribosomal, 80 non-ribosomal and 5700 synthetic peptides. Of these, the vast majority are monomeric, but nearly 200 of these peptides are found as homo- or heterodimers. More than 6100 of the peptides are linear, but about 515 are cyclic and more than 1300 have other intra-chain covalent bonds. More than half of the entries in the database were added after the resource was initially described, which reflects the recent sharp uptick of interest in AMPs. New features of DBAASPv.2 include: (i) user-friendly utilities and reporting functions, (ii) a ‘Ranking Search’ function to query the database by target species and return a ranked list of peptides with activity against that target and (iii) structural descriptions of the peptides derived from empirical data or calculated by molecular dynamics (MD) simulations. The three-dimensional structural data are critical components for understanding structure–activity relationships and for design of new antimicrobial drugs. We created more than 300 high-throughput MD simulations specifically for inclusion in DBAASP. The resulting structures are described in the database by novel trajectory analysis plots and movies. Another 200+ DBAASP entries have links to the Protein DataBank. All of the structures are easily visualized directly in the web browser.

Selectivity in the Use of Gi/o Proteins Is Determined by the DRF Motif in CXCR6 and Is Cell-Type Specific

CXCR6, the receptor for CXCL16, is expressed on multiple cell types and can be a coreceptor for human immunodeficiency virus 1. Except for CXCR6, all human chemokine receptors contain the D(3.49)R(3.50)Y(3.51) sequence, and all but two contain A(3.53) at the cytoplasmic terminus of the third transmembrane helix (H3C), a region within class A G protein-coupled receptors that contacts G proteins. In CXCR6, H3C contains D(3.49)R(3.50)F(3.51)I(3.52)V(3.53) at positions 126-130. We investigated the importance and interdependence of the canonical D126 and the noncanonical F128 and V130 in CXCR6 by mutating D126 to Y, F128 to Y, and V130 to A singly and in combination. For comparison, we mutated the analogous positions D142, Y144, and A146 to Y, F, and V, respectively, in CCR6, a related receptor containing the canonical sequences. Mutants were analyzed in both human embryonic kidney 293T and Jurkat E6-1 cells. Our data show that for CXCR6 and/or CCR6, mutations in H3C can affect both receptor signaling and chemokine binding; noncanonical H3C sequences are functionally linked, with dual changes mitigating the effects of single mutations; mutations in H3C that compromise receptor activity show selective defects in the use of individual Gi/o proteins; and the effects of mutations in H3C on receptor function and selectivity in Gi/o protein use can be cell-type specific. Our findings indicate that the ability of CXCR6 to make promiscuous use of the available Gi/o proteins is exquisitely dependent on sequences within the H3C and suggest that the native sequence allows for preservation of this function across different cellular environments.

The NIH 3D Print Exchange: A Public Resource for Bioscientific and Biomedical 3D Prints

The National Institutes of Health (NIH) has launched the NIH 3D Print Exchange, an online portal for discovering and creating bioscientifically relevant 3D models suitable for 3D printing, to provide both researchers and educators with a trusted source to discover accurate and informative models. There are a number of online resources for 3D prints, but there is a paucity of scientific models, and the expertise required to generate and validate such models remains a barrier. The NIH 3D Print Exchange fills this gap by providing novel, web-based tools that empower users with the ability to create ready-to-print 3D files from molecular structure data, microscopy image stacks, and computed tomography scan data. The NIH 3D Print Exchange facilitates open data sharing in a community-driven environment, and also includes various interactive features, as well as information and tutorials on 3D modeling software. As the first government-sponsored website dedicated to 3D printing, the NIH 3D Print Exchange is an important step forward to bringing 3D printing to the mainstream for scientific research and education.

A novel point mutation of the human glucocorticoid receptor gene causes primary generalized glucocorticoid resistance through impaired interaction with the LXXLL motif of the p160 coactivators: dissociation of the transactivating and transreppressive activities

CONTEXT

Primary generalized glucocorticoid resistance is a rare genetic disorder characterized by generalized, partial, target-tissue insensitivity to glucocorticoids. The molecular basis of the condition has been ascribed to inactivating mutations in the human glucocorticoid receptor (hGR) gene.

OBJECTIVE

The objective of the study was to present three new cases caused by a novel mutation in the hGR gene and to delineate the molecular mechanisms through which the mutant receptor impairs glucocorticoid signal transduction.

DESIGN AND RESULTS

The index case (father) and his two daughters presented with increased urinary free cortisol excretion and resistance of the hypothalamic-pituitary-adrenal axis to dexamethasone suppression in the absence of clinical manifestations suggestive of Cushing syndrome. All subjects harbored a novel, heterozygous, point mutation (T→G) at nucleotide position 1724 of the hGR gene, which resulted in substitution of valine by glycine at amino acid 575 of the receptor. Compared with the wild-type receptor, the hGRαV575G demonstrated a significant (33%) reduction in its ability to transactivate the mouse mammary tumor virus promoter in response to dexamethasone, a 50% decrease in its affinity for the ligand, and a 2.5-fold delay in nuclear translocation. Although it did not exert a dominant negative effect on the wild-type receptor and preserved its ability to bind to DNA, hGRαV575G displayed significantly enhanced (∼80%) ability to transrepress the nuclear factor-κΒ signaling pathway. Finally, the mutant receptor hGRαV575G demonstrated impaired interaction with the LXXLL motif of the glucocorticoid receptor-interacting protein 1 coactivator in vitro and in computer-based structural simulation via its defective activation function-2 (AF-2) domain.

CONCLUSIONS

The natural mutant receptor hGRαV575G causes primary generalized glucocorticoid resistance by affecting multiple steps in the glucocorticoid signaling cascade, including the affinity for the ligand, the time required for nuclear translocation, and the interaction with the glucocorticoid-interacting protein-1 coactivator.

HASP server: a database and structural visualization platform for comparative models of influenza A hemagglutinin proteins

Background

Influenza A viruses possess RNA genomes that mutate frequently in response to immune pressures. The mutations in the hemagglutinin genes are particularly significant, as the hemagglutinin proteins mediate attachment and fusion to host cells, thereby influencing viral pathogenicity and species specificity. Large-scale influenza A genome sequencing efforts have been ongoing to understand past epidemics and pandemics and anticipate future outbreaks. Sequencing efforts thus far have generated nearly 9,000 distinct hemagglutinin amino acid sequences.

Description

Comparative models for all publicly available influenza A hemagglutinin protein sequences (8,769 to date) were generated using the Rosetta modeling suite. The C-alpha root mean square deviations between a randomly chosen test set of models and their crystallographic templates were less than 2 Å, suggesting that the modeling protocols yielded high-quality results. The models were compiled into an online resource, the Hemagglutinin Structure Prediction (HASP) server. The HASP server was designed as a scientific tool for researchers to visualize hemagglutinin protein sequences of interest in a three-dimensional context. With a built-in molecular viewer, hemagglutinin models can be compared side-by-side and navigated by a corresponding sequence alignment. The models and alignments can be downloaded for offline use and further analysis.

Conclusions

The modeling protocols used in the HASP server scale well for large amounts of sequences and will keep pace with expanded sequencing efforts. The conservative approach to modeling and the intuitive search and visualization interfaces allow researchers to quickly analyze hemagglutinin sequences of interest in the context of the most highly related experimental structures, and allow them to directly compare hemagglutinin sequences to each other simultaneously in their two- and three-dimensional contexts. The models and methodology have shown utility in current research efforts and the ongoing aim of the HASP server is to continue to accelerate influenza A research and have a positive impact on global public health.

A novel point mutation in the DNA-binding domain (DBD) of the human glucocorticoid receptor causes primary generalized glucocorticoid resistance by disrupting the hydrophobic structure of its DBD

CONTEXT

Primary generalized glucocorticoid resistance is a rare genetic condition characterized by partial end-organ insensitivity to glucocorticoids. Most affected subjects present with clinical manifestations of mineralocorticoid and androgen excess. The condition has been associated with inactivating mutations in the human glucocorticoid receptor (hGR) gene, which impair the molecular mechanisms of hGRα action, thereby reducing tissue sensitivity to glucocorticoids.

OBJECTIVE

ΤHE aim of our study was to investigate the molecular mechanisms through which one previously described natural heterozygous V423A mutation, the second mutation detected in the DNA-binding domain (DBD) of the hGRα, affects glucocorticoid signal transduction.

DESIGN AND RESULTS

Compared with the wild-type receptor, hGRαV423A demonstrated a 72% reduction in its ability to transactivate the glucocorticoid-inducible mouse mammary tumor virus promoter in response to dexamethasone. The hGRαV423A receptor showed a significant reduction in its ability to bind to glucocorticoid-response elements of glucocorticoid-responsive genes, owing to structural alterations of the DBD confirmed by computer-based structural analysis. In addition, hGRαV423A demonstrated a 2.6-fold delay in nuclear translocation following exposure to the ligand, although it did not exert a dominant negative effect on the wild-type hGRα, had a similar affinity to the ligand with the wild-type receptor, and displayed a normal interaction with the GRIP1 coactivator in vitro.

CONCLUSIONS

The natural mutant receptor hGRαV423A causes primary generalized glucocorticoid resistance by affecting multiple steps in the cascade of glucocorticoid receptor action, which primarily involve decreased ability to bind to target glucocorticoid response elements and delayed translocation into the nucleus.

A novel point mutation in the KCNJ5 gene causing primary hyperaldosteronism and early-onset autosomal dominant hypertension

CONTEXT

Aldosterone production in the adrenal zona glomerulosa is mainly regulated by angiotensin II, [K(+)], and ACTH. Genetic deletion of subunits of K(+)-selective leak (KCNK) channels TWIK-related acid sensitive K(+)-1 and/or TWIK-related acid sensitive K(+)-3 in mice results in primary hyperaldosteronism, whereas mutations in the KCNJ5 (potassium inwardly rectifying channel, subfamily J, member 5) gene are implicated in primary hyperaldosteronism and, in certain cases, in autonomous glomerulosa cell proliferation in humans.

OBJECTIVE

The objective of the study was to investigate the role of KCNK3, KCNK5, KCNK9, and KCNJ5 genes in a family with primary hyperaldosteronism and early-onset hypertension.

PATIENTS AND METHODS

Two patients, a mother and a daughter, presented with severe primary hyperaldosteronism, bilateral massive adrenal hyperplasia, and early-onset hypertension refractory to medical treatment. Genomic DNA was isolated and the exons of the entire coding regions of the above genes were amplified and sequenced. Electrophysiological studies were performed to determine the effect of identified mutation(s) on the membrane reversal potentials.

RESULTS

Sequencing of the KCNJ5 gene revealed a single, heterozygous guanine to thymine (G → T) substitution at nucleotide position 470 (n.G470T), resulting in isoleucine (I) to serine (S) substitution at amino acid 157 (p.I157S). This mutation results in loss of ion selectivity, cell membrane depolarization, increased Ca(2+) entry in adrenal glomerulosa cells, and increased aldosterone synthesis. Sequencing of the KCNK3, KCNK5, and KCNK9 genes revealed no mutations in our patients.

CONCLUSIONS

These findings explain the pathogenesis in a subset of patients with severe hypertension and implicate loss of K(+) channel selectivity in constitutive aldosterone production.

Determination of molecular structures of HIV envelope glycoproteins using cryo-electron tomography and automated sub-tomogram averaging

Since its discovery nearly 30 years ago, more than 60 million people have been infected with the human immunodeficiency virus (HIV) (www.usaid.gov). The virus infects and destroys CD4+ T-cells thereby crippling the immune system, and causing an acquired immunodeficiency syndrome (AIDS) ². Infection begins when the HIV Envelope glycoprotein "spike" makes contact with the CD4 receptor on the surface of the CD4+ T-cell. This interaction induces a conformational change in the spike, which promotes interaction with a second cell surface co-receptor ^5,9. The significance of these protein interactions in the HIV infection pathway makes them of profound importance in fundamental HIV research, and in the pursuit of an HIV vaccine.

The need to better understand the molecular-scale interactions of HIV cell contact and neutralization motivated the development of a technique to determine the structures of the HIV spike interacting with cell surface receptor proteins and molecules that block infection. Using cryo-electron tomography and 3D image processing, we recently demonstrated the ability to determine such structures on the surface of native virus, at ˜20 Å resolution ^9,14. This approach is not limited to resolving HIV Envelope structures, and can be extended to other viral membrane proteins and proteins reconstituted on a liposome. In this protocol, we describe how to obtain structures of HIV envelope glycoproteins starting from purified HIV virions and proceeding stepwise through preparing vitrified samples, collecting, cryo-electron microscopy data, reconstituting and processing 3D data volumes, averaging and classifying 3D protein subvolumes, and interpreting results to produce a protein model. The computational aspects of our approach were adapted into modules that can be accessed and executed remotely using the Biowulf GNU/Linux parallel processing cluster at the NIH (http://biowulf.nih.gov). This remote access, combined with low-cost computer hardware and high-speed network access, has made possible the involvement of researchers and students working from school or home.

Glycosylation focuses sequence variation in the influenza A virus H1 hemagglutinin globular domain

Antigenic drift in the influenza A virus hemagglutinin (HA) is responsible for seasonal reformulation of influenza vaccines. Here, we address an important and largely overlooked issue in antigenic drift: how does the number and location of glycosylation sites affect HA evolution in man? We analyzed the glycosylation status of all full-length H1 subtype HA sequences available in the NCBI influenza database. We devised the "flow index" (FI), a simple algorithm that calculates the tendency for viruses to gain or lose consensus glycosylation sites. The FI predicts the predominance of glycosylation states among existing strains. Our analyses show that while the number of glycosylation sites in the HA globular domain does not influence the overall magnitude of variation in defined antigenic regions, variation focuses on those regions unshielded by glycosylation. This supports the conclusion that glycosylation generally shields HA from antibody-mediated neutralization, and implies that fitness costs in accommodating oligosaccharides limit virus escape via HA hyperglycosylation.

A novel point mutation in helix 10 of the human glucocorticoid receptor causes generalized glucocorticoid resistance by disrupting the structure of the ligand-binding domain

CONTEXT

Generalized glucocorticoid resistance syndrome is a rare familial or sporadic condition characterized by partial insensitivity to glucocorticoids, caused by mutations in the glucocorticoid receptor (GR) gene. Most of the reported cases are adults, demonstrating symptoms associated with mineralocorticoid and/or adrenal androgen excess caused by compensatively increased secretion of the adrenocorticotropic hormone.

PATIENT

We identified a new 2-yr-old female case of generalized glucocorticoid resistance syndrome. The patient (TJ) presented with a generalized seizure associated with hypoglycemia and hypokalemia. She also had hypertension and premature pubarche, whereas dexamethasone effectively suppressed these clinical manifestations.

RESULTS

The patient's GR gene had a heterozygotic mutation (G-->A) at nucleotide position 2141 (exon 8), which resulted in substitution of arginine by glutamine at amino acid position 714 in the ligand-binding domain (LBD) of the GR alpha. Molecular analysis revealed that the mutant receptor had significantly impaired transactivation activity with a 2-fold reduction in affinity to ligand. It showed attenuated transactivation of the activation function (AF)-2 and reduced binding to a p160 nuclear receptor coactivator. Computer-based structural analysis revealed that replacement of arginine by glutamine at position 714 transmitted a conformational change to the LBD and the AF-2 transactivation surface, resulting in a decreased binding affinity to ligand and to the LXXLL coactivator motif.

CONCLUSIONS

Dexamethasone treatment is effective in controlling the premature pubarche, hypoglycemia, hypertension, and hypokalemia in this child case, wherein arginine 714 plays a key role in the proper formation of the ligand-binding pocket and the AF-2 surface of the GR alpha LBD.

Noncoding RNA gas5 is a growth arrest- and starvation-associated repressor of the glucocorticoid receptor

The availability of nutrients influences cellular growth and survival by affecting gene transcription. Glucocorticoids also influence gene transcription and have diverse activities on cell growth, energy expenditure, and survival. We found that the growth arrest-specific 5 (Gas5) noncoding RNA, which is abundant in cells whose growth has been arrested because of lack of nutrients or growth factors, sensitized cells to apoptosis by suppressing glucocorticoid-mediated induction of several responsive genes, including the one encoding cellular inhibitor of apoptosis 2. Gas5 bound to the DNA-binding domain of the glucocorticoid receptor (GR) by acting as a decoy glucocorticoid response element (GRE), thus competing with DNA GREs for binding to the GR. We conclude that Gas5 is a "riborepressor" of the GR, influencing cell survival and metabolic activities during starvation by modulating the transcriptional activity of the GR.

Structure of the Plasmodium falciparum circumsporozoite protein, a leading malaria vaccine candidate

The Plasmodium falciparum circumsporozoite protein (CSP) is critical for sporozoite function and invasion of hepatocytes. Given its critical nature, a phase III human CSP malaria vaccine trial is ongoing. The CSP is composed of three regions as follows: an N terminus that binds heparin sulfate proteoglycans, a four amino acid repeat region (NANP), and a C terminus that contains a thrombospondin-like type I repeat (TSR) domain. Despite the importance of CSP, little is known about its structure. Therefore, recombinant forms of CSP were produced by expression in both Escherichia coli (Ec) and then refolded (EcCSP) or in the methylotrophic yeast Pichia pastoris (PpCSP) for structural analyses. To analyze the TSR domain of recombinant CSP, conformation-dependent monoclonal antibodies that recognized unfixed P. falciparum sporozoites and inhibited sporozoite invasion of HepG2 cells in vitro were identified. These monoclonal antibodies recognized all recombinant CSPs, indicating the recombinant CSPs contain a properly folded TSR domain structure. Characterization of both EcCSP and PpCSP by dynamic light scattering and velocity sedimentation demonstrated that both forms of CSP appeared as highly extended proteins (R(h) 4.2 and 4.58 nm, respectively). Furthermore, high resolution atomic force microscopy revealed flexible, rod-like structures with a ribbon-like appearance. Using this information, we modeled the NANP repeat and TSR domain of CSP. Consistent with the biochemical and biophysical results, the repeat region formed a rod-like structure about 21-25 nm in length and 1.5 nm in width. Thus native CSP appears as a glycosylphosphatidylinositol-anchored, flexible rod-like protein on the sporozoite surface.

Characterization of a protective Escherichia coli-expressed Plasmodium falciparum merozoite surface protein 3 indicates a non-linear, multi-domain structure

Immunization with a recombinant yeast-expressed Plasmodium falciparum merozoite surface protein 3 (MSP3) protected Aotus nancymai monkeys against a virulent challenge infection. Unfortunately, the production process for this yeast-expressed material was not optimal for human trials. In an effort to produce a recombinant MSP3 protein in a scaleable manner, we expressed and purified near-full-length MSP3 in Escherichia coli (EcMSP3). Purified EcMSP3 formed non-globular dimers as determined by analytical size-exclusion HPLC with in-line multi-angle light scatter and quasi-elastic light scatter detection and velocity sedimentation (R(h) 7.6+/-0.2nm and 6.9nm, respectively). Evaluation by high-resolution atomic force microscopy revealed non-linear asymmetric structures, with beaded domains and flexible loops that were recognized predominantly as dimers, although monomers and larger multimers were observed. The beaded substructure corresponds to predicted structural domains, which explains the velocity sedimentation results and improves the conceptual model of the protein. Vaccination with EcMSP3 in Freund's adjuvant-induced antibodies that recognized native MSP3 in parasitized erythrocytes by an immunofluorescence assay and gave delayed time to treatment in a group of Aotus monkeys in a virulent challenge infection with the FVO strain of P. falciparum. Three of the seven monkeys vaccinated with EcMSP3 had low peak parasitemias. EcMSP3, which likely mimics the native MSP3 structure located on the merozoite surface, is a viable candidate for inclusion in a multi-component malaria vaccine.

Structure ofPlasmodium falciparumdihydroorotate dehydrogenase with a bound inhibitor

Membrane-associated dihydroorotate dehydrogenase (DHODH) is an antimalarial therapeutic target without an effective inhibitor. Studies on human DHODH (HsDHODH) led to a structural mechanistic model in which respiratory quinones bind in a tunnel formed by the highly variable N-terminus that leads to the flavin mononucleotide-binding site. The therapeutic agents leflunomide (Arava) and brequinar sodium inhibit HsDHODH by binding in this tunnel. Plasmodium falciparum DHODH (PfDHODH) and HsDHODH have markedly different sensitivities to the two drugs. To understand the structural basis of this differential sensitivity and begin a structure-based drug-design cycle for PfDHODH inhibitors, the three-dimensional structure (2.4 Angstroms, R = 20.1%) of PfDHODH bound to the active metabolite of leflunomide was determined by X-ray crystallography. Comparison of the structures of HsDHODH and PfDHODH reveals a completely different binding mode for the same inhibitor in these two catalytically identical enzymes and explains the previously observed species-specific preferential binding. Because no effective inhibitors have been described for PfDHODH, this structure provides critical insight for the design of potential antimalarials.

Brequinar derivatives and species-specific drug design for dihydroorotate dehydrogenase

Therapeutic agents brequinar sodium and leflunomide (Arava) work by binding in a hydrophobic tunnel formed by a highly variable N-terminus of family 2 dihydroorotate dehydrogenase (DHODH). The X-ray crystallographic structure of an analog of brequinar bound to human DHODH was determined. In silico screening of a library of compounds suggested another subset of brequinar analogs that do not inhibit human DHODH as potentially effective inhibitors of Plasmodium falciparum DHODH.