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.

Molecular structure of antibodies against small ligands

Antibodies which bind bioactive ligands can serve as a template for the generation of a second antibody which may react with the physiological receptor. This phenomenon of molecular mimicry by antibodies has been described in a variety of systems. In order to understand the chemical and molecular mechanisms involved in these interactions, monoclonal antibodies directed against two pharmacologically active alkaloids, morphine and nicotine, were carefully studied using experimental and theoretical molecular modeling techniques. The molecular characterization of these antibodies involved binding studies with ligand analogs and determination of the variable region amino acid sequence. A three-dimensional model of the anti-morphine binding site was constructed using computational and graphics display techniques. The antibody response in BALB/c mice to morphine appears relatively restricted, in that all of the antibodies examined in this study contained a $\lambda$ light chain, which is normally found in only 5% of mouse immunoglobulins. This study represents the first use of theoretical and experimental modeling techniques to describe the antigen binding site of a mouse Fv region containing a $\lambda$ light chain. The binding site model indicates that a charged glutamic acid residue and aromatic side chains are key features in ionic and hydrophobic interactions with the ligand morphine. A glutamic acid residue is found in the identical position in the anti-nicotine antibody and may play a role in binding nicotine. ^

Molecular mechanisms of prostacyclin receptor signaling: The intracellular domains in G protein coupling

To better understand the mechanisms of how the human prostacyclin receptor (1P) mediates vasodilation and platelet anti-aggregation through Gs protein coupling, a strategy integrating multiple approaches including high resolution NMR experiments, synthetic peptide, fluorescence spectroscopy, molecular modeling, and recombinant protein was developed and used to characterize the structure/function relationship of important segments and residues of the IP receptor and the α-subunit of the Gs protein (Gαs). The first (iLP1) and third (iLP3) intracellular loops of the IP receptor, as well as the Gαs C-terminal domain, relevant to the Gs-mediated IP receptor signaling, were first identified by observation of the effects of the mini gene-expressed corresponding protein segments in HEK293 cells which co-expressed the receptor and Gαs. Evidence of the IP iLP1 domain interacted with the Gαs C-terminal domain was observed by fluorescence and NMR spectroscopic studies using a constrained synthetic peptide, which mimicked the IP iLP1 domain, and the synthetic peptide, which mimicked Gαs C-terminal domain. The solution structural models and the peptide-peptide interaction of the two synthetic protein segments were determined by high resolution NMR spectroscopy. The important residues in the corresponding domains of the IP receptor and the Gαs predicted by NMR chemical shift mapping were used to guide the identification of their protein-protein interaction in cells. A profile of the residues Arg42 - Ala48 of the IP iLP1 domain and the three residues Glu392 ∼ Leu394 of the Gαs C-terminal domain involved in the IP/Gs protein coupling were confirmed by recombinant proteins. The data revealed an intriguing speculation on the mechanisms of how the signal of the ligand-activated IP receptor is transmitted to the Gs protein in regulating vascular functions and homeostasis, and also provided substantial insights into other prostanoid receptor signaling. ^

TIME SERIES ANALYSIS AS INPUT FOR PREDICTIVE MODELING: PREDICTING CARDIAC ARREST IN A PEDIATRIC INTENSIVE CARE UNIT

The first manuscript, entitled "Time-Series Analysis as Input for Clinical Predictive Modeling: Modeling Cardiac Arrest in a Pediatric ICU" lays out the theoretical background for the project. There are several core concepts presented in this paper. First, traditional multivariate models (where each variable is represented by only one value) provide single point-in-time snapshots of patient status: they are incapable of characterizing deterioration. Since deterioration is consistently identified as a precursor to cardiac arrests, we maintain that the traditional multivariate paradigm is insufficient for predicting arrests. We identify time series analysis as a method capable of characterizing deterioration in an objective, mathematical fashion, and describe how to build a general foundation for predictive modeling using time series analysis results as latent variables. Building a solid foundation for any given modeling task involves addressing a number of issues during the design phase. These include selecting the proper candidate features on which to base the model, and selecting the most appropriate tool to measure them. We also identified several unique design issues that are introduced when time series data elements are added to the set of candidate features. One such issue is in defining the duration and resolution of time series elements required to sufficiently characterize the time series phenomena being considered as candidate features for the predictive model. Once the duration and resolution are established, there must also be explicit mathematical or statistical operations that produce the time series analysis result to be used as a latent candidate feature. In synthesizing the comprehensive framework for building a predictive model based on time series data elements, we identified at least four classes of data that can be used in the model design. The first two classes are shared with traditional multivariate models: multivariate data and clinical latent features. Multivariate data is represented by the standard one value per variable paradigm and is widely employed in a host of clinical models and tools. These are often represented by a number present in a given cell of a table. Clinical latent features derived, rather than directly measured, data elements that more accurately represent a particular clinical phenomenon than any of the directly measured data elements in isolation. The second two classes are unique to the time series data elements. The first of these is the raw data elements. These are represented by multiple values per variable, and constitute the measured observations that are typically available to end users when they review time series data. These are often represented as dots on a graph. The final class of data results from performing time series analysis. This class of data represents the fundamental concept on which our hypothesis is based. The specific statistical or mathematical operations are up to the modeler to determine, but we generally recommend that a variety of analyses be performed in order to maximize the likelihood that a representation of the time series data elements is produced that is able to distinguish between two or more classes of outcomes. The second manuscript, entitled "Building Clinical Prediction Models Using Time Series Data: Modeling Cardiac Arrest in a Pediatric ICU" provides a detailed description, start to finish, of the methods required to prepare the data, build, and validate a predictive model that uses the time series data elements determined in the first paper. One of the fundamental tenets of the second paper is that manual implementations of time series based models are unfeasible due to the relatively large number of data elements and the complexity of preprocessing that must occur before data can be presented to the model. Each of the seventeen steps is analyzed from the perspective of how it may be automated, when necessary. We identify the general objectives and available strategies of each of the steps, and we present our rationale for choosing a specific strategy for each step in the case of predicting cardiac arrest in a pediatric intensive care unit. Another issue brought to light by the second paper is that the individual steps required to use time series data for predictive modeling are more numerous and more complex than those used for modeling with traditional multivariate data. Even after complexities attributable to the design phase (addressed in our first paper) have been accounted for, the management and manipulation of the time series elements (the preprocessing steps in particular) are issues that are not present in a traditional multivariate modeling paradigm. In our methods, we present the issues that arise from the time series data elements: defining a reference time; imputing and reducing time series data in order to conform to a predefined structure that was specified during the design phase; and normalizing variable families rather than individual variable instances. The final manuscript, entitled: "Using Time-Series Analysis to Predict Cardiac Arrest in a Pediatric Intensive Care Unit" presents the results that were obtained by applying the theoretical construct and its associated methods (detailed in the first two papers) to the case of cardiac arrest prediction in a pediatric intensive care unit. Our results showed that utilizing the trend analysis from the time series data elements reduced the number of classification errors by 73%. The area under the Receiver Operating Characteristic curve increased from a baseline of 87% to 98% by including the trend analysis. In addition to the performance measures, we were also able to demonstrate that adding raw time series data elements without their associated trend analyses improved classification accuracy as compared to the baseline multivariate model, but diminished classification accuracy as compared to when just the trend analysis features were added (ie, without adding the raw time series data elements). We believe this phenomenon was largely attributable to overfitting, which is known to increase as the ratio of candidate features to class examples rises. Furthermore, although we employed several feature reduction strategies to counteract the overfitting problem, they failed to improve the performance beyond that which was achieved by exclusion of the raw time series elements. Finally, our data demonstrated that pulse oximetry and systolic blood pressure readings tend to start diminishing about 10-20 minutes before an arrest, whereas heart rates tend to diminish rapidly less than 5 minutes before an arrest.

Characterization of anti -sulfatide autoantibodies in demyelinating diseases

Multiple sclerosis (MS) is the most common autoimmune disease of the central nerve system and Guillain Barré Syndrome (GBS) is an inflammatory neuropathy involving the peripheral nerves. Anti-myelin immunoglobins may play a role in the demyelination processes of the both diseases. Sulfatide is an abundant glycolipid on myelin and is a candidate target antigen for disease related autoantibodies. The objective of this study was to characterize anti-sulfatide antibodies and compare antibodies from GBS and MS patients with fetal antibodies. Our hypothesis is that some B cells producing disease-associated autoantibodies are derived from or related to B cells of the fetal repertoire. Here we report that reactivity of plasma IgM against sulfatide was elevated in twelve MS patients compared with twelve normal subjects. This result implies that anti-sulfatide antibodies are disease-related. A total of sixteen human B lymphocyte clones producing anti-sulfatide autoantibodies were isolated from MS patients, GBS patients and a human fetus. Seven of the clones were from three MS patients, four of the clones were from three GBS patients and five were from the spleen of a twenty-week human fetus. Sequences have been obtained for the heavy and light chain variable regions (VDJ and VJ regions) of all of the anti-sulfatide immunoglobulins. Seven of the sixteen antibodies used VH3 for the variable region gene of the heavy chain consistent with the rate of VH3 usage in randomly selected B cells. Somatic mutations were significantly more frequent in the patient antibodies than in the fetus and somatic mutations in CDR's (Complementarity Determining Region) were significantly more frequent than in framework regions. No significant difference was found between patients and fetus in length of VH CDRIII. However, it is reported that antibodies from randomly selected normal adult B cells have longer CDRIII lengths than those of the fetus (Sanz I, 1991 Journal of Immunology Sep 1;147(5):1720-9). Our results are consistent with derivation of the precursors of B cells producing these autoantibodies from B cells related to those of the fetal repertoire. These findings are consistent with a model in which quiescent B cells from clones produced early in development undergo proliferation in dysregulated disease states, accumulating somatic mutations and increasing in reactivity toward self-antigens. ^ Epitope mapping and molecular modeling were done to elucidate the relationships between antibody structure and binding characteristics. The autoantibodies were tested for binding activity to three different antigens: sulfatide, galactoceramide and ceramide. Molecular modeling suggests that antibodies with positive charge surrounded by or adjacent to hydrophobic groups in the binding pocket bind to the head of sulfatide via the sulfate group through electrostatic interactions. However, the antibodies with hydrophobic groups separated from positive charges appear to bind to the hydrophobic tail of sulfatide. This observation was supported by a study of the effect of NaCl concentration on antigen binding. The result suggested that electrostatic interactions played a major role in sulfate group binding and that hydrophobic interactions were of greater importance for binding to the ceramide group. Our three-dimensional structure data indicated that epitope specificity of these antibodies is more predictable at the level of tertiary than primary structure and suggested positive selection based on structure occurred in the. formation of those autoantibodies. ^

Bifurcation and singularity analysis of a molecular network for the induction of long-term memory.

Withdrawal reflexes of the mollusk Aplysia exhibit sensitization, a simple form of long-term memory (LTM). Sensitization is due, in part, to long-term facilitation (LTF) of sensorimotor neuron synapses. LTF is induced by the modulatory actions of serotonin (5-HT). Pettigrew et al. developed a computational model of the nonlinear intracellular signaling and gene network that underlies the induction of 5-HT-induced LTF. The model simulated empirical observations that repeated applications of 5-HT induce persistent activation of protein kinase A (PKA) and that this persistent activation requires a suprathreshold exposure of 5-HT. This study extends the analysis of the Pettigrew model by applying bifurcation analysis, singularity theory, and numerical simulation. Using singularity theory, classification diagrams of parameter space were constructed, identifying regions with qualitatively different steady-state behaviors. The graphical representation of these regions illustrates the robustness of these regions to changes in model parameters. Because persistent protein kinase A (PKA) activity correlates with Aplysia LTM, the analysis focuses on a positive feedback loop in the model that tends to maintain PKA activity. In this loop, PKA phosphorylates a transcription factor (TF-1), thereby increasing the expression of an ubiquitin hydrolase (Ap-Uch). Ap-Uch then acts to increase PKA activity, closing the loop. This positive feedback loop manifests multiple, coexisting steady states, or multiplicity, which provides a mechanism for a bistable switch in PKA activity. After the removal of 5-HT, the PKA activity either returns to its basal level (reversible switch) or remains at a high level (irreversible switch). Such an irreversible switch might be a mechanism that contributes to the persistence of LTM. The classification diagrams also identify parameters and processes that might be manipulated, perhaps pharmacologically, to enhance the induction of memory. Rational drug design, to affect complex processes such as memory formation, can benefit from this type of analysis.

Predictive oncology: a review of multidisciplinary, multiscale in silico modeling linking phenotype, morphology and growth.

Empirical evidence and theoretical studies suggest that the phenotype, i.e., cellular- and molecular-scale dynamics, including proliferation rate and adhesiveness due to microenvironmental factors and gene expression that govern tumor growth and invasiveness, also determine gross tumor-scale morphology. It has been difficult to quantify the relative effect of these links on disease progression and prognosis using conventional clinical and experimental methods and observables. As a result, successful individualized treatment of highly malignant and invasive cancers, such as glioblastoma, via surgical resection and chemotherapy cannot be offered and outcomes are generally poor. What is needed is a deterministic, quantifiable method to enable understanding of the connections between phenotype and tumor morphology. Here, we critically assess advantages and disadvantages of recent computational modeling efforts (e.g., continuum, discrete, and cellular automata models) that have pursued this understanding. Based on this assessment, we review a multiscale, i.e., from the molecular to the gross tumor scale, mathematical and computational "first-principle" approach based on mass conservation and other physical laws, such as employed in reaction-diffusion systems. Model variables describe known characteristics of tumor behavior, and parameters and functional relationships across scales are informed from in vitro, in vivo and ex vivo biology. We review the feasibility of this methodology that, once coupled to tumor imaging and tumor biopsy or cell culture data, should enable prediction of tumor growth and therapy outcome through quantification of the relation between the underlying dynamics and morphological characteristics. In particular, morphologic stability analysis of this mathematical model reveals that tumor cell patterning at the tumor-host interface is regulated by cell proliferation, adhesion and other phenotypic characteristics: histopathology information of tumor boundary can be inputted to the mathematical model and used as a phenotype-diagnostic tool to predict collective and individual tumor cell invasion of surrounding tissue. This approach further provides a means to deterministically test effects of novel and hypothetical therapy strategies on tumor behavior.

Cellular dynamic simulator: an event driven molecular simulation environment for cellular physiology.

In this paper, we present the Cellular Dynamic Simulator (CDS) for simulating diffusion and chemical reactions within crowded molecular environments. CDS is based on a novel event driven algorithm specifically designed for precise calculation of the timing of collisions, reactions and other events for each individual molecule in the environment. Generic mesh based compartments allow the creation / importation of very simple or detailed cellular structures that exist in a 3D environment. Multiple levels of compartments and static obstacles can be used to create a dense environment to mimic cellular boundaries and the intracellular space. The CDS algorithm takes into account volume exclusion and molecular crowding that may impact signaling cascades in small sub-cellular compartments such as dendritic spines. With the CDS, we can simulate simple enzyme reactions; aggregation, channel transport, as well as highly complicated chemical reaction networks of both freely diffusing and membrane bound multi-protein complexes. Components of the CDS are generally defined such that the simulator can be applied to a wide range of environments in terms of scale and level of detail. Through an initialization GUI, a simple simulation environment can be created and populated within minutes yet is powerful enough to design complex 3D cellular architecture. The initialization tool allows visual confirmation of the environment construction prior to execution by the simulator. This paper describes the CDS algorithm, design implementation, and provides an overview of the types of features available and the utility of those features are highlighted in demonstrations.

Molecular basis for class V beta-tubulin effects on microtubule assembly and paclitaxel resistance.

Vertebrates produce at least seven distinct beta-tubulin isotypes that coassemble into all cellular microtubules. The functional differences among these tubulin isoforms are largely unknown, but recent studies indicate that tubulin composition can affect microtubule properties and cellular microtubule-dependent behavior. One of the isotypes whose incorporation causes the largest change in microtubule assembly is beta5-tubulin. Overexpression of this isotype can almost completely destroy the microtubule network, yet it appears to be required in smaller amounts for normal mitotic progression. Moderate levels of overexpression can also confer paclitaxel resistance. Experiments using chimeric constructs and site-directed mutagenesis now indicate that the hypervariable C-terminal region of beta5 plays no role in these phenotypes. Instead, we demonstrate that two residues found in beta5 (Ser-239 and Ser-365) are each sufficient to inhibit microtubule assembly and confer paclitaxel resistance when introduced into beta1-tubulin; yet the single mutation of residue Ser-239 in beta5 eliminates its ability to confer these phenotypes. Despite the high degree of conservation among beta-tubulin isotypes, mutations affecting residue 365 demonstrate that amino acid substitutions can be context sensitive; i.e. an amino acid change in one isotype will not necessarily produce the same phenotype when introduced into a different isotype. Modeling studies indicate that residue Cys-239 of beta1-tubulin is close to a highly conserved Cys-354 residue suggesting the possibility that disulfide formation could play a significant role in the stability of microtubules formed with beta1- but not with beta5-tubulin.

Molecular markers of human papillomavirus (HPV) infection and epidemiologic risk factors for squamous intraepithelial lesions (SIL) of the cervix

Infection with certain types of HPV is a necessary event in the development of cervical carcinoma; however, not all women who become infected will progress. While much is known about the molecular influence of HPV E6 and E7 proteins on the malignant transformation, little is known about the additional factors needed to drive the process. Currently, conventional cervical screening is insufficient at identifying women who are likely to progress from premalignant lesions to carcinoma. Aneuploidy and chromatin texture from image cytometry have been suggested as quantitative measures of nuclear damage in premalignant lesions and cancer, and traditional epidemiologic studies have identified potential factors to aid in the discrimination of those lesions likely to progress. ^ In the current study, real-time PCR was used to quantitate mRNA expression of the E7 gene in women exhibiting normal epithelium, LSIL, and HSIL. Quantitative cytometry was used to gather information about the DNA index and chromatin features of cells from the same women. Logistic regression modeling was used to establish predictor variables for histologic grade based on the traditional epidemiologic risk factors and molecular markers. ^ Prevalence of mRNA transcripts was lower among women with normal histology (27%) than for women with LSIL (40%) and HSIL (37%) with mean levels ranging from 2.0 to 4.2. The transcriptional activity of HPV 18 was higher than that of HPV 16 and increased with increasing level of dysplasia, reinforcing the more aggressive nature of HPV 18. DNA index and mRNA level increased with increasing histological grade. Chromatin score was not correlated with histology but was higher for HPV 18 samples and those with both HPV 18 and HPV 16. However, chromatin score and DNA index were not correlated with mRNA levels. The most predictive variables in the regression modeling were mRNA level, DNA index, parity, and age, and the ROC curves for LSIL and HSIL indicated excellent discrimination. ^ Real-time PCR of viral transcripts could provide a more efficient method to analyze the oncogenic potential within cells from cervical swabs. Epidemiological modeling of malignant progression in the cervix should include molecular markers, as well as the traditional epidemiological risk factors. ^

A coalescent analysis for modeling the mutation process in colorectal cancer

Colorectal cancer is the forth most common diagnosed cancer in the United States. Every year about a hundred forty-seven thousand people will be diagnosed with colorectal cancer and fifty-six thousand people lose their lives due to this disease. Most of the hereditary nonpolyposis colorectal cancer (HNPCC) and 12% of the sporadic colorectal cancer show microsatellite instability. Colorectal cancer is a multistep progressive disease. It starts from a mutation in a normal colorectal cell and grows into a clone of cells that further accumulates mutations and finally develops into a malignant tumor. In terms of molecular evolution, the process of colorectal tumor progression represents the acquisition of sequential mutations. ^ Clinical studies use biomarkers such as microsatellite or single nucleotide polymorphisms (SNPs) to study mutation frequencies in colorectal cancer. Microsatellite data obtained from single genome equivalent PCR or small pool PCR can be used to infer tumor progression. Since tumor progression is similar to population evolution, we used an approach known as coalescent, which is well established in population genetics, to analyze this type of data. Coalescent theory has been known to infer the sample's evolutionary path through the analysis of microsatellite data. ^ The simulation results indicate that the constant population size pattern and the rapid tumor growth pattern have different genetic polymorphic patterns. The simulation results were compared with experimental data collected from HNPCC patients. The preliminary result shows the mutation rate in 6 HNPCC patients range from 0.001 to 0.01. The patients' polymorphic patterns are similar to the constant population size pattern which implies the tumor progression is through multilineage persistence instead of clonal sequential evolution. The results should be further verified using a larger dataset. ^

An integrated molecular biology and computational approach to CYP1A1 expression in human brain

The cytochromes P450 comprise a superfamily of heme-containing mono-oxygenases. These enzymes metabolize numerous xenobiotics, but also play a role in metabolism of endogenous compounds. The P450 1A1 enzyme generally metabolizes polycyclic aromatic hydrocarbons, and its expression can be induced by aryl hydrocarbon receptor (AhR) activation. CYP1A1 is an exception to the generality that the majority of CYPs demonstrate highest expression in liver; CYP1Al is present in numerous extrahepatic tissues, including brain. This P450 has been observed in two forms, wildtype (WT) and brain variant (BV), arising from alternatively spliced mRNA transcripts. The CYP1A1 BV mRNA presented an exon deletion and was detected in human brain but not liver tissue of the same individuals. ^ Quantitative PCR analyses were performed to determine CYP1A1 WT and BV transcript expression levels in normal, bipolar disorder or schizophrenic groups. In our samples, we show that CYP1A1 BV mRNA, when present, is found alongside the full-length form. Furthermore, we demonstrate a significant decrease in expression of CYP1A1 in patients with bipolar disorder or schizophrenia. The expression level was not influenced by post-mortem interval, tissue pH, age, tobacco use, or lifetime antipsychotic medication load. ^ There is no indication of increased brain CYP1A1 expression in normal smokers versus non-smokers in these samples. We observed slightly increased CYP1A1 expression only in bipolar and schizophrenic smokers versus non-smokers. This may be indicative of complex interactions between neuronal chemical environments and AhR-mediated CYP1A1 induction in brain. ^ Structural homology modeling demonstrated that P450 1A1 BV has several alterations to positions/orientations of substrate recognition site residues compared to the WT isoform. Automated substrate docking was employed to investigate the potential binding of neurological signaling molecules and neurotropic drugs, as well as to differentiate specificities of the two P450 1A1 isoforms. We consistently observed that the BV isoform produced energetically favorable substrate dockings in orientations not observed for the same substrate in the WT isoform. These results demonstrated that structural differences, namely an expanded substrate access channel and active site, confer greater capacity for unique compound docking positions suggesting a metabolic profile distinct from the wildtype form for these test compounds. ^