Modeling Techniques for the High-Resolution Interpretation of Cryo-Electron Microscopy Reconstructions

Essential biological processes are governed by organized, dynamic interactions between multiple biomolecular systems. Complexes are thus formed to enable the biological function and get dissembled as the process is completed. Examples of such processes include the translation of the messenger RNA into protein by the ribosome, the folding of proteins by chaperonins or the entry of viruses in host cells. Understanding these fundamental processes by characterizing the molecular mechanisms that enable then, would allow the (better) design of therapies and drugs. Such molecular mechanisms may be revealed trough the structural elucidation of the biomolecular assemblies at the core of these processes. Various experimental techniques may be applied to investigate the molecular architecture of biomolecular assemblies. High-resolution techniques, such as X-ray crystallography, may solve the atomic structure of the system, but are typically constrained to biomolecules of reduced flexibility and dimensions. In particular, X-ray crystallography requires the sample to form a three dimensional (3D) crystal lattice which is technically di‑cult, if not impossible, to obtain, especially for large, dynamic systems. Often these techniques solve the structure of the different constituent components within the assembly, but encounter difficulties when investigating the entire system. On the other hand, imaging techniques, such as cryo-electron microscopy (cryo-EM), are able to depict large systems in near-native environment, without requiring the formation of crystals. The structures solved by cryo-EM cover a wide range of resolutions, from very low level of detail where only the overall shape of the system is visible, to high-resolution that approach, but not yet reach, atomic level of detail. In this dissertation, several modeling methods are introduced to either integrate cryo-EM datasets with structural data from X-ray crystallography, or to directly interpret the cryo-EM reconstruction. Such computational techniques were developed with the goal of creating an atomic model for the cryo-EM data. The low-resolution reconstructions lack the level of detail to permit a direct atomic interpretation, i.e. one cannot reliably locate the atoms or amino-acid residues within the structure obtained by cryo-EM. Thereby one needs to consider additional information, for example, structural data from other sources such as X-ray crystallography, in order to enable such a high-resolution interpretation. Modeling techniques are thus developed to integrate the structural data from the different biophysical sources, examples including the work described in the manuscript I and II of this dissertation. At intermediate and high-resolution, cryo-EM reconstructions depict consistent 3D folds such as tubular features which in general correspond to alpha-helices. Such features can be annotated and later on used to build the atomic model of the system, see manuscript III as alternative. Three manuscripts are presented as part of the PhD dissertation, each introducing a computational technique that facilitates the interpretation of cryo-EM reconstructions. The first manuscript is an application paper that describes a heuristics to generate the atomic model for the protein envelope of the Rift Valley fever virus. The second manuscript introduces the evolutionary tabu search strategies to enable the integration of multiple component atomic structures with the cryo-EM map of their assembly. Finally, the third manuscript develops further the latter technique and apply it to annotate consistent 3D patterns in intermediate-resolution cryo-EM reconstructions. The first manuscript, titled An assembly model for Rift Valley fever virus, was submitted for publication in the Journal of Molecular Biology. The cryo-EM structure of the Rift Valley fever virus was previously solved at 27Å-resolution by Dr. Freiberg and collaborators. Such reconstruction shows the overall shape of the virus envelope, yet the reduced level of detail prevents the direct atomic interpretation. High-resolution structures are not yet available for the entire virus nor for the two different component glycoproteins that form its envelope. However, homology models may be generated for these glycoproteins based on similar structures that are available at atomic resolutions. The manuscript presents the steps required to identify an atomic model of the entire virus envelope, based on the low-resolution cryo-EM map of the envelope and the homology models of the two glycoproteins. Starting with the results of the exhaustive search to place the two glycoproteins, the model is built iterative by running multiple multi-body refinements to hierarchically generate models for the different regions of the envelope. The generated atomic model is supported by prior knowledge regarding virus biology and contains valuable information about the molecular architecture of the system. It provides the basis for further investigations seeking to reveal different processes in which the virus is involved such as assembly or fusion. The second manuscript was recently published in the of Journal of Structural Biology (doi:10.1016/j.jsb.2009.12.028) under the title Evolutionary tabu search strategies for the simultaneous registration of multiple atomic structures in cryo-EM reconstructions. This manuscript introduces the evolutionary tabu search strategies applied to enable a multi-body registration. This technique is a hybrid approach that combines a genetic algorithm with a tabu search strategy to promote the proper exploration of the high-dimensional search space. Similar to the Rift Valley fever virus, it is common that the structure of a large multi-component assembly is available at low-resolution from cryo-EM, while high-resolution structures are solved for the different components but lack for the entire system. Evolutionary tabu search strategies enable the building of an atomic model for the entire system by considering simultaneously the different components. Such registration indirectly introduces spatial constrains as all components need to be placed within the assembly, enabling the proper docked in the low-resolution map of the entire assembly. Along with the method description, the manuscript covers the validation, presenting the benefit of the technique in both synthetic and experimental test cases. Such approach successfully docked multiple components up to resolutions of 40Å. The third manuscript is entitled Evolutionary Bidirectional Expansion for the Annotation of Alpha Helices in Electron Cryo-Microscopy Reconstructions and was submitted for publication in the Journal of Structural Biology. The modeling approach described in this manuscript applies the evolutionary tabu search strategies in combination with the bidirectional expansion to annotate secondary structure elements in intermediate resolution cryo-EM reconstructions. In particular, secondary structure elements such as alpha helices show consistent patterns in cryo-EM data, and are visible as rod-like patterns of high density. The evolutionary tabu search strategy is applied to identify the placement of the different alpha helices, while the bidirectional expansion characterizes their length and curvature. The manuscript presents the validation of the approach at resolutions ranging between 6 and 14Å, a level of detail where alpha helices are visible. Up to resolution of 12 Å, the method measures sensitivities between 70-100% as estimated in experimental test cases, i.e. 70-100% of the alpha-helices were correctly predicted in an automatic manner in the experimental data. The three manuscripts presented in this PhD dissertation cover different computation methods for the integration and interpretation of cryo-EM reconstructions. The methods were developed in the molecular modeling software Sculptor (http://sculptor.biomachina.org) and are available for the scientific community interested in the multi-resolution modeling of cryo-EM data. The work spans a wide range of resolution covering multi-body refinement and registration at low-resolution along with annotation of consistent patterns at high-resolution. Such methods are essential for the modeling of cryo-EM data, and may be applied in other fields where similar spatial problems are encountered, such as medical imaging.

THE NATURAL AND ORTHOGONAL INTERACTION (NOIA) MODELS FOR QUANTITATIVE TRAITS (QTs) AND COMPLEX DISEASES

My dissertation focuses on developing methods for gene-gene/environment interactions and imprinting effect detections for human complex diseases and quantitative traits. It includes three sections: (1) generalizing the Natural and Orthogonal interaction (NOIA) model for the coding technique originally developed for gene-gene (GxG) interaction and also to reduced models; (2) developing a novel statistical approach that allows for modeling gene-environment (GxE) interactions influencing disease risk, and (3) developing a statistical approach for modeling genetic variants displaying parent-of-origin effects (POEs), such as imprinting. In the past decade, genetic researchers have identified a large number of causal variants for human genetic diseases and traits by single-locus analysis, and interaction has now become a hot topic in the effort to search for the complex network between multiple genes or environmental exposures contributing to the outcome. Epistasis, also known as gene-gene interaction is the departure from additive genetic effects from several genes to a trait, which means that the same alleles of one gene could display different genetic effects under different genetic backgrounds. In this study, we propose to implement the NOIA model for association studies along with interaction for human complex traits and diseases. We compare the performance of the new statistical models we developed and the usual functional model by both simulation study and real data analysis. Both simulation and real data analysis revealed higher power of the NOIA GxG interaction model for detecting both main genetic effects and interaction effects. Through application on a melanoma dataset, we confirmed the previously identified significant regions for melanoma risk at 15q13.1, 16q24.3 and 9p21.3. We also identified potential interactions with these significant regions that contribute to melanoma risk. Based on the NOIA model, we developed a novel statistical approach that allows us to model effects from a genetic factor and binary environmental exposure that are jointly influencing disease risk. Both simulation and real data analyses revealed higher power of the NOIA model for detecting both main genetic effects and interaction effects for both quantitative and binary traits. We also found that estimates of the parameters from logistic regression for binary traits are no longer statistically uncorrelated under the alternative model when there is an association. Applying our novel approach to a lung cancer dataset, we confirmed four SNPs in 5p15 and 15q25 region to be significantly associated with lung cancer risk in Caucasians population: rs2736100, rs402710, rs16969968 and rs8034191. We also validated that rs16969968 and rs8034191 in 15q25 region are significantly interacting with smoking in Caucasian population. Our approach identified the potential interactions of SNP rs2256543 in 6p21 with smoking on contributing to lung cancer risk. Genetic imprinting is the most well-known cause for parent-of-origin effect (POE) whereby a gene is differentially expressed depending on the parental origin of the same alleles. Genetic imprinting affects several human disorders, including diabetes, breast cancer, alcoholism, and obesity. This phenomenon has been shown to be important for normal embryonic development in mammals. Traditional association approaches ignore this important genetic phenomenon. In this study, we propose a NOIA framework for a single locus association study that estimates both main allelic effects and POEs. We develop statistical (Stat-POE) and functional (Func-POE) models, and demonstrate conditions for orthogonality of the Stat-POE model. We conducted simulations for both quantitative and qualitative traits to evaluate the performance of the statistical and functional models with different levels of POEs. Our results showed that the newly proposed Stat-POE model, which ensures orthogonality of variance components if Hardy-Weinberg Equilibrium (HWE) or equal minor and major allele frequencies is satisfied, had greater power for detecting the main allelic additive effect than a Func-POE model, which codes according to allelic substitutions, for both quantitative and qualitative traits. The power for detecting the POE was the same for the Stat-POE and Func-POE models under HWE for quantitative traits.

STIMULATION THROUGH TLR4 INCREASES FVIII INHIBITOR FORMATION IN A MOUSE MODEL OF HEMOPHILIA A

Hemophilia A is a clotting disorder caused by functional factor VIII (FVIII) deficiency. About 25% of patients treated with therapeutic recombinant FVIII develop antibodies (inhibitors) that render subsequent FVIII treatments ineffective. The immune mechanisms of inhibitor formation are not entirely understood, but circumstantial evidence indicates a role for increased inflammatory response, possibly via stimulation of Toll-like receptors (TLRs), at the time of FVIII immunization. I hypothesized that stimulation through TLR4 in conjunction with FVIII treatments would increase the formation of FVIII inhibitors. To test this hypothesis, FVIII K.O. mice were injected with recombinant human FVIII with or without concomitant doses of TLR4 agonist (lipopoysaccharide; LPS). The addition of LPS combined with FVIII significantly increased the rate and the production of anti-FVIII IgG antibodies and neutralizing FVIII inhibitors. In the spleen, repeated in vivo TLR4 stimulation with LPS increased the relative percentage of macrophages and dendritic cells (DCs) over the course of 4 injections. However, repeated in vivo FVIII stimulation significantly increased the density of TLR4 expressed on the surface of all spleen antigen presenting cells (APCs). Culture of splenocytes isolated from mice revealed that the combined stimulation of LPS and FVIII also synergistically increased early secretion of the inflammatory cytokines IL-6, TNF-α, and IL-10, which was not maintained throughout the course of the repeated injections. While cytokine secretion was relatively unchanged in response to FVIII re-stimulation in culture, LPS re-stimulation in culture induced increased and prolonged inflammatory cytokine secretion. Re-stimulation with both LPS and FVIII induced cytokine secretion similar to LPS stimulation alone. Interestingly, long term treatment of mice with LPS alone resulted in splenocytes that showed reduced response to FVIII in culture. Together these results indicated that creating a pro-inflammatory environment through the combined stimulation of chronic, low-dose LPS and FVIII changed not only the populations but also the repertoire of APCs in the spleen, triggering the increased production of FVIII inhibitors. These results suggested an anti-inflammatory regimen should be instituted for all hemophilia A patients to reduce or delay the formation of FVIII inhibitors during replacement therapy.

Development of HIF-1α/HIF-1β heterodimerization inhibitors using a novel bioluminescence reporter assay system for in vitro high throughput screening and in vivo imaging

Tumor growth often outpaces its vascularization, leading to development of a hypoxic tumor microenvironment. In response, an intracellular hypoxia survival pathway is initiated by heterodimerization of hypoxia-inducible factor (HIF)-1α and HIF-1β, which subsequently upregulates the expression of several hypoxia-inducible genes, promotes cell survival and stimulates angiogenesis in the oxygen-deprived environment. Hypoxic tumor regions are often associated with resistance to various classes of radio- or chemotherapeutic agents. Therefore, development of HIF-1α/β heterodimerization inhibitors may provide a novel approach to anti-cancer therapy. To this end, a novel approach for imaging HIF-1α/β heterodimerization in vitro and in vivo was developed in this study. Using this screening platform, we identified a promising lead candidate and further chemically derivatized the lead candidate to assess the structure-activity relationship (SAR). The most effective first generation drug inhibitors were selected and their pharmacodynamics and anti-tumor efficacy in vivo were verified by bioluminescence imaging (BLI) of HIF-1α/β heterodimerization in the xenograft tumor model. Furthermore, the first generation drug inhibitors, M-TMCP and D-TMCP, demonstrated efficacy as monotherapies, resulting in tumor growth inhibition via disruption of HIF-1 signaling-mediated tumor stromal neoangiogenesis.

PHENOTHIAZINES INDUCE ABORTIVE AUTOPHAGY LEADING TO CANCER CELL DEATH

Autophagy is an evolutionarily conserved process that functions to maintain homeostasis and provides energy during nutrient deprivation and environmental stresses for the survival of cells by delivering cytoplasmic contents to the lysosomes for recycling and energy generation. Dysregulation of this process has been linked to human diseases including immune disorders, neurodegenerative muscular diseases and cancer. Autophagy is a double edged sword in that it has both pro-survival and pro-death roles in cancer cells. Its cancer suppressive roles include the clearance of damaged organelles, which could otherwise lead to inflammation and therefore promote tumorigenesis. In its pro-survival role, autophagy allows cancer cells to overcome cytotoxic stresses generated the cancer environment or cancer treatments such as chemotherapy and evade cell death. A better understanding of how drugs that perturb autophagy affect cancer cell signaling is of critical importance toimprove the cancer treatment arsenal. In order to gain insights in the relationship between autophagy and drug treatments, we conducted a high-throughput drug screen to identify autophagy modulators. Our high-throughput screen utilized image based fluorescent microscopy for single cell analysis to identify chemical perturbants of the autophagic process. Phenothiazines emerged as the largest family of drugs that alter the autophagic process by increasing LC3-II punctae levels in different cancer cell lines. In addition, we observed multiple biological effects in cancer cells treated with phenothiazines. Those antitumorigenic effects include decreased cell migration, cell viability, and ATP production along with abortive autophagy. Our studies highlight the potential role of phenothiazines as agents for combinational therapy with other chemotherapeutic agents in the treatment of different cancers.

UNDERSTANDING NURSE CREATED COGNITIVE ARTIFACTS: PERSONALLY-CREATED-COGNITIVE-ARTIFACTS AS EXTERNAL REPRESENTATIONS OF DISTRIBUTED COGNITION

Manuscript 1: “Conceptual Analysis: Externalizing Nursing Knowledge” We use concept analysis to establish that the report tool nurses prepare, carry, reference, amend, and use as a temporary data repository are examples of cognitive artifacts. This tool, integrally woven throughout the work and practice of nurses, is important to cognition and clinical decision-making. Establishing the tool as a cognitive artifact will support new dimensions of study. Such studies can characterize how this report tool supports cognition, internal representation of knowledge and skills, and external representation of knowledge of the nurse. Manuscript 2: “Research Methods: Exploring Cognitive Work” The purpose of this paper is to describe a complex, cross-sectional, multi-method approach to study of personal cognitive artifacts in the clinical environment. The complex data arrays present in these cognitive artifacts warrant the use of multiple methods of data collection. Use of a less robust research design may result in an incomplete understanding of the meaning, value, content, and relationships between personal cognitive artifacts in the clinical environment and the cognitive work of the user. Manuscript 3: “Making the Cognitive Work of Registered Nurses Visible” Purpose: Knowledge representations and structures are created and used by registered nurses to guide patient care. Understanding is limited regarding how these knowledge representations, or cognitive artifacts, contribute to working memory, prioritization, organization, cognition, and decision-making. The purpose of this study was to identify and characterize the role a specific cognitive artifact knowledge representation and structure as it contributed to the cognitive work of the registered nurse. Methods: Data collection was completed, using qualitative research methods, by shadowing and interviewing 25 registered nurses. Data analysis employed triangulation and iterative analytic processes. Results: Nurse cognitive artifacts support recall, data evaluation, decision-making, organization, and prioritization. These cognitive artifacts demonstrated spatial, longitudinal, chronologic, visual, and personal cues to support the cognitive work of nurses. Conclusions: Nurse cognitive artifacts are an important adjunct to the cognitive work of nurses, and directly support patient care. Nurses need to be able to configure their cognitive artifact in ways that are meaningful and support their internal knowledge representations.