Michael T. Zimmermann

Crystal structures of the CusA efflux pump suggest methionine-mediated metal transport

Gram-negative bacteria, such as Escherichia coli, frequently use tripartite efflux complexes in the resistance-nodulation-cell division (RND) family to expel various toxic compounds from the cell. The efflux system CusCBA is responsible for extruding biocidal Cu(I) and Ag(I) ions. No previous structural information was available for the heavy-metal efflux (HME) subfamily of the RND efflux pumps. Here we describe the crystal structures of the inner-membrane transporter CusA in the absence and presence of bound Cu(I) or Ag(I). These CusA structures provide new structural information about the HME subfamily of RND efflux pumps. The structures suggest that the metal-binding sites, formed by a three-methionine cluster, are located within the cleft region of the periplasmic domain. This cleft is closed in the apo-CusA form but open in the CusA-Cu(I) and CusA-Ag(I) structures, which directly suggests a plausible pathway for ion export. Binding of Cu(I) and Ag(I) triggers significant conformational changes in both the periplasmic and transmembrane domains. The crystal structure indicates that CusA has, in addition to the three-methionine metal-binding site, four methionine pairs—three located in the transmembrane region and one in the periplasmic domain. Genetic analysis and transport assays suggest that CusA is capable of actively picking up metal ions from the cytosol, using these methionine pairs or clusters to bind and export metal ions. These structures suggest a stepwise shuttle mechanism for transport between these sites.

Crystal structure of the CusBA heavy-metal efflux complex of Escherichia coli

Gram-negative bacteria, such as Escherichia coli, expel toxic chemicals through tripartite efflux pumps that span both the inner and outer membrane. The three parts are an inner membrane, substrate-binding transporter; a membrane fusion protein; and an outer-membrane-anchored channel. The fusion protein connects the transporter to the channel within the periplasmic space. A crystallographic model of this tripartite efflux complex has been unavailable because co-crystallization of the various components of the system has proven to be extremely difficult. We previously described the crystal structures of both the inner membrane transporter CusA and the membrane fusion protein CusB of the CusCBA efflux system of E. coli. Here we report the co-crystal structure of the CusBA efflux complex, showing that the transporter (or pump) CusA, which is present as a trimer, interacts with six CusB protomers and that the periplasmic domain of CusA is involved in these interactions. The six CusB molecules seem to form a continuous channel. The affinity of the CusA and CusB interaction was found to be in the micromolar range. Finally, we have predicted a three-dimensional structure for the trimeric CusC outer membrane channel and developed a model of the tripartite efflux assemblage. This CusC3–CusB6–CusA3 model shows a 750-kilodalton efflux complex that spans the entire bacterial cell envelope and exports Cu i and Ag i ions.

ICGA-PSO-ELM Approach for Accurate Multiclass Cancer Classification Resulting in Reduced Gene Sets in Which Genes Encoding Secreted Proteins Are Highly Represented

A combination of Integer-Coded Genetic Algorithm (ICGA) and Particle Swarm Optimization (PSO), coupled with the neural-network-based Extreme Learning Machine (ELM), is used for gene selection and cancer classification. ICGA is used with PSO-ELM to select an optimal set of genes, which is then used to build a classifier to develop an algorithm (ICGA_PSO_ELM) that can handle sparse data and sample imbalance. We evaluate the performance of ICGA-PSO-ELM and compare our results with existing methods in the literature. An investigation into the functions of the selected genes, using a systems biology approach, revealed that many of the identified genes are involved in cell signaling and proliferation. An analysis of these gene sets shows a larger representation of genes that encode secreted proteins than found in randomly selected gene sets. Secreted proteins constitute a major means by which cells interact with their surroundings. Mounting biological evidence has identified the tumor microenvironment as a critical factor that determines tumor survival and growth. Thus, the genes identified by this study that encode secreted proteins might provide important insights to the nature of the critical biological features in the microenvironment of each tumor type that allow these cells to thrive and proliferate.

Human telomerase model shows the role of the TEN domain in advancing the double helix for the next polymerization step

Telomerases constitute a group of specialized ribonucleoprotein enzymes that remediate chromosomal shrinkage resulting from the “end-replication” problem. Defects in telomere length regulation are associated with several diseases as well as with aging and cancer. Despite significant progress in understanding the roles of telomerase, the complete structure of the human telomerase enzyme bound to telomeric DNA remains elusive, with the detailed molecular mechanism of telomere elongation still unknown. By application of computational methods for distant homology detection, comparative modeling, and molecular docking, guided by available experimental data, we have generated a three-dimensional structural model of a partial telomerase elongation complex composed of three essential protein domains bound to a single-stranded telomeric DNA sequence in the form of a heteroduplex with the template region of the human RNA subunit, TER. This model provides a structural mechanism for the processivity of telomerase and offers new insights into elongation. We conclude that the RNA∶DNA heteroduplex is constrained by the telomerase TEN domain through repeated extension cycles and that the TEN domain controls the process by moving the template ahead one base at a time by translation and rotation of the double helix. The RNA region directly following the template can bind complementarily to the newly synthesized telomeric DNA, while the template itself is reused in the telomerase active site during the next reaction cycle. This first structural model of the human telomerase enzyme provides many details of the molecular mechanism of telomerase and immediately provides an important target for rational drug design.

MACE: model based analysis of ChIP-exo

Understanding the role of a given transcription factor (TF) in regulating gene expression requires precise mapping of its binding sites in the genome. Chromatin immunoprecipitation-exo, an emerging technique using λ exonuclease to digest TF unbound DNA after ChIP, is designed to reveal transcription factor binding site (TFBS) boundaries with near-single nucleotide resolution. Although ChIP-exo promises deeper insights into transcription regulation, no dedicated bioinformatics tool exists to leverage its advantages. Most ChIP-seq and ChIP-chip analytic methods are not tailored for ChIP-exo, and thus cannot take full advantage of high-resolution ChIP-exo data. Here we describe a novel analysis framework, termed MACE (model-based analysis of ChIP-exo) dedicated to ChIP-exo data analysis. The MACE workflow consists of four steps: (i) sequencing data normalization and bias correction; (ii) signal consolidation and noise reduction; (iii) single-nucleotide resolution border peak detection using the Chebyshev Inequality and (iv) border matching using the Gale-Shapley stable matching algorithm. When applied to published human CTCF, yeast Reb1 and our own mouse ONECUT1/HNF6 ChIP-exo data, MACE is able to define TFBSs with high sensitivity, specificity and spatial resolution, as evidenced by multiple criteria including motif enrichment, sequence conservation, direct sequence pileup, nucleosome positioning and open chromatin states. In addition, we show that the fundamental advance of MACE is the identification of two boundaries of a TFBS with high resolution, whereas other methods only report a single location of the same event. The two boundaries help elucidate the in vivo binding structure of a given TF, e.g. whether the TF may bind as dimers or in a complex with other co-factors.

MAVENs: Motion analysis and visualization of elastic networks and structural ensembles

BackgroundThe ability to generate, visualize, and analyze motions of biomolecules has made a significant impact upon modern biology. Molecular Dynamics has gained substantial use, but remains computationally demanding and difficult to setup for many biologists. Elastic network models (ENMs) are an alternative and have been shown to generate the dominant equilibrium motions of biomolecules quickly and efficiently. These dominant motions have been shown to be functionally relevant and also to indicate the likely direction of conformational changes. Most structures have a small number of dominant motions. Comparing computed motions to the structures conformational ensemble derived from a collection of static structures or frames from an MD trajectory is an important way to understand functional motions as well as evaluate the models. Modes of motion computed from ENMs can be visualized to gain functional and mechanistic understanding and to compute useful quantities such as average positional fluctuations, internal distance changes, collectiveness of motions, and directional correlations within the structure.ResultsOur new software, MAVEN, aims to bring ENMs and their analysis to a broader audience by integrating methods for their generation and analysis into a user friendly environment that automates many of the steps. Models can be constructed from raw PDB files or density maps, using all available atomic coordinates or by employing various coarse-graining procedures. Visualization can be performed either with our software or exported to molecular viewers. Mixed resolution models allow one to study atomic effects on the system while retaining much of the computational speed of the coarse-grained ENMs. Analysis options are available to further aid the user in understanding the computed motions and their importance for its function.ConclusionMAVEN has been developed to simplify ENM generation, allow for diverse models to be used, and facilitate useful analyses, all on the same platform. This represents an integrated approach that incorporates all four levels of the modeling process - generation, evaluation, analysis, visualization - and also brings to bear multiple ENM types. The intension is to provide a versatile modular suite of programs to a broader audience. MAVEN is available for download at http://maven.sourceforge.net.

Whole-exome analysis reveals novel somatic genomic alterations associated with outcome in immunochemotherapy-treated diffuse large B-cell lymphoma

Lack of remission or early relapse remains a major clinical issue in diffuse large B-cell lymphoma (DLBCL), with 30% of patients failing standard of care. Although clinical factors and molecular signatures can partially predict DLBCL outcome, additional information is needed to identify high-risk patients, particularly biologic factors that might ultimately be amenable to intervention. Using whole-exome sequencing data from 51 newly diagnosed and immunochemotherapy-treated DLBCL patients, we evaluated the association of somatic genomic alterations with patient outcome, defined as failure to achieve event-free survival at 24 months after diagnosis (EFS24). We identified 16 genes with mutations, 374 with copy number gains and 151 with copy number losses that were associated with failure to achieve EFS24 (P<0.05). Except for FOXO1 and CIITA, known driver mutations did not correlate with EFS24. Gene losses were localized to 6q21-6q24.2, and gains to 3q13.12-3q29, 11q23.1-11q23.3 and 19q13.12-19q13.43. Globally, the number of gains was highly associated with poor outcome (P=7.4 × 10−12) and when combined with FOXO1 mutations identified 77% of cases that failed to achieve EFS24. One gene (SLC22A16) at 6q21, a doxorubicin transporter, was lost in 54% of EFS24 failures and our findings suggest it functions as a doxorubicin transporter in DLBCL cells.

Computational and experimental characterization of RNA cubic nanoscaffolds

The fast-developing field of RNA nanotechnology requires the adoption and development of novel and faster computational approaches to modeling and characterization of RNA-based nano-objects. We report the first application of Elastic Network Modeling (ENM), a structure-based dynamics model, to RNA nanotechnology. With the use of an Anisotropic Network Model (ANM), a type of ENM, we characterize the dynamic behavior of non-compact, multi-stranded RNA-based nanocubes that can be used as nano-scale scaffolds carrying different functionalities. Modeling the nanocubes with our tool NanoTiler and exploring the dynamic characteristics of the models with ANM suggested relatively minor but important structural modifications that enhanced the assembly properties and thermodynamic stabilities. In silico and in vitro, we compared nanocubes having different numbers of base pairs per side, showing with both methods that the 10 bp-long helix design leads to more efficient assembly, as predicted computationally. We also explored the impact of different numbers of single-stranded nucleotide stretches at each of the cube corners and showed that cube flexibility simulations help explain the differences in the experimental assembly yields, as well as the measured nanomolecule sizes and melting temperatures. This original work paves the way for detailed computational analysis of the dynamic behavior of artificially designed multi-stranded RNA nanoparticles.

Novel Type of Renal Amyloidosis Derived from Apolipoprotein-CII

Amyloidosis is characterized by extracellular deposition of misfolded proteins as insoluble fibrils. Most renal amyloidosis cases are Ig light chain, AA, or leukocyte chemotactic factor 2 amyloidosis, but rare hereditary forms can also involve the kidneys. Here, we describe the case of a 61-year-old woman who presented with nephrotic syndrome and renal impairment. Examination of the renal biopsy specimen revealed amyloidosis with predominant involvement of glomeruli and medullary interstitium. Proteomic analysis of Congo red-positive deposits detected large amounts of the Apo-CII protein. DNA sequencing of the APOC2 gene in the patient and one of her children detected a heterozygous c.206A→T transition, causing an E69V missense mutation. We also detected the mutant peptide in the probands renal amyloid deposits. Using proteomics, we identified seven additional elderly patients with Apo-CII-rich amyloid deposits, all of whom had kidney involvement and histologically exhibited nodular glomerular involvement. Although prior in vitro studies have shown that Apo-CII can form amyloid fibrils and that certain mutations in this protein promote amyloid fibrillogenesis, there are no reports of this type of amyloidosis in humans. We propose that this study reveals a new form of hereditary amyloidosis (AApoCII) that is derived from the Apo-CII protein and appears to manifest in the elderly and preferentially affect the kidneys.

Understanding Protein–Nanoparticle Interaction:A New Gateway to Disease Therapeutics

Molecular identification of protein molecules surrounding nanoparticles (NPs) may provide useful information that influences NP clearance, biodistribution, and toxicity. Hence, nanoproteomics provides specific information about the environment that NPs interact with and can therefore report on the changes in protein distribution that occurs during tumorigenesis. Therefore, we hypothesized that characterization and identification of protein molecules that interact with 20 nm AuNPs from cancer and noncancer cells may provide mechanistic insights into the biology of tumor growth and metastasis and identify new therapeutic targets in ovarian cancer. Hence, in the present study, we systematically examined the interaction of the protein molecules with 20 nm AuNPs from cancer and noncancerous cell lysates. Time-resolved proteomic profiles of NP-protein complexes demonstrated electrostatic interaction to be the governing factor in the initial time-points which are dominated by further stabilization interaction at longer time-points as determined by ultraviolet–visible spectroscopy (UV–vis), dynamic light scattering (DLS), ζ-potential measurements, transmission electron microscopy (TEM), and tandem mass spectrometry (MS/MS). Reduction in size, charge, and number of bound proteins were observed as the protein-NP complex stabilized over time. Interestingly, proteins related to mRNA processing were overwhelmingly represented on the NP-protein complex at all times. More importantly, comparative proteomic analyses revealed enrichment of a number of cancer-specific proteins on the AuNP surface. Network analyses of these proteins highlighted important hub nodes that could potentially be targeted for maximal therapeutic advantage in the treatment of ovarian cancer. The importance of this methodology and the biological significance of the network proteins were validated by a functional study of three hubs that exhibited variable connectivity, namely, PPA1, SMNDC1, and PI15. Western blot analysis revealed overexpression of these proteins in ovarian cancer cells when compared to normal cells. Silencing of PPA1, SMNDC1, and PI15 by the siRNA approach significantly inhibited proliferation of ovarian cancer cells and the effect correlated with the connectivity pattern obtained from our network analyses.