A Collaborative Initiative to Establish Genomic Biomarkers for Assessing Tumorigenic Potential to Reduce Reliance on Conventional Rodent Carcinogenicity Studies.

There is growing recognition across broad sectors of the scientific community that use of genomic biomarkers has the potential to reduce the need for conventional rodent carcinogenicity studies of industrial chemicals, agrochemicals, and pharmaceuticals through a weight-of-evidence approach. These biomarkers fall into 2 major categories: (1) sets of gene transcripts that can identify distinct tumorigenic mechanisms of action; and (2) cancer driver gene mutations indicative of rapidly expanding growth-advantaged clonal cell populations. This call-to-action article describes a collaborative approach launched to develop and qualify biomarker gene expression panels that measure widely accepted molecular pathways linked to tumorigenesis and their activation levels to predict tumorigenic doses of chemicals from short-term exposures. Growing evidence suggests that application of such biomarker panels in short-term exposure rodent studies can identify both tumorigenic hazard and tumorigenic activation levels for chemical-induced carcinogenicity. In the future, this approach will be expanded to include methodologies examining mutations in key cancer driver gene mutation hotspots as biomarkers of both genotoxic and nongenotoxic chemical tumor risk. Analytical, technical, and biological validation studies of these complementary genomic tools are being undertaken by multisector and multidisciplinary collaborative teams within the Health and Environmental Sciences Institute. Success from these efforts will facilitate the transition from current heavy reliance on conventional 2-year rodent carcinogenicity studies to more rapid animal- and resource-sparing approaches for mechanism-based carcinogenicity evaluation supporting internal and regulatory decision-making.

Understanding the effect of increased cell specific productivity on galactosylation of monoclonal antibodies produced using Chinese hamster ovary cells.

Achieving optimal productivity and desired product quality of the therapeutic monoclonal antibody (mAb) is one of the primary goals of process development. Across the various mAb programs at our company, we observed that increasing the specific productivity (qp) results in a decrease in the % galactosylation (%Gal) level on the protein. In order to gain further insight into this correlation, cells were cultured under different process conditions such as pH or media osmolality or in the presence of supplements such as sodium butyrate. A range of qp and N-glycan profiles were obtained with the greatest changes observed under high pH (lower qp, higher %Gal), higher osmolality (higher qp, lower %Gal) or sodium butyrate (moderately higher qp, moderately lower %Gal) conditions. Abundance of individual glycan species highlighted different bottlenecks in the N-glycosylation pathway depending on the treatment condition. Transcriptomics analysis was performed to identify changes in gene expression profiles that correlate with the inverse relationship between qp and %Gal. Results showed downregulation of Beta-1,4-galactosyltransferase 1 (B4GalT1), UDP-GlcNAc and Mn2+ transporter (slc35a3 and slc39a8 respectively) for the high osmolality conditions. Significant downregulation of slc39a8 (Mn2+ transporter) was observed for the sodium butyrate condition. No significant differences were observed for any of the genes in the N-glycosylation pathway under the high pH condition even though this condition showed highest %Gal. Together, data suggests that different treatments have distinct complex mechanisms by which the overall glycan levels of a mAb are influenced. Further studies based on these results will help build the knowledge necessary to design strategies to obtain the desired productivity and product quality of mAbs.

Development and Application of a Transcriptomic Signature of Bioactivation in an Advanced In Vitro Liver Model to Reduce Drug-induced Liver Injury Risk Early in the Pharmaceutical Pipeline.

Early risk assessment of drug-induced liver injury (DILI) potential for drug candidates remains a major challenge for pharmaceutical development. We have previously developed a set of rat liver transcriptional biomarkers in short-term toxicity studies to inform the potential of drug candidates to generate a high burden of chemically reactive metabolites that presents higher risk for human DILI. Here, we describe translation of those NRF1-/NRF2-mediated liver tissue biomarkers to an in vitro assay using an advanced micropatterned coculture system (HEPATOPAC) with primary hepatocytes from male Wistar Han rats. A 9-day, resource-sparing and higher throughput approach designed to identify new chemical entities with lower reactive metabolite-forming potential was qualified for internal decision making using 93 DILI-positive and -negative drugs. This assay provides 81% sensitivity and 90% specificity in detecting hepatotoxicants when a positive test outcome is defined as the bioactivation signature score of a test drug exceeding the threshold value at an in vitro test concentration that falls within 3-fold of the estimated maximum drug concentration at the human liver inlet following highest recommended clinical dose administrations. Using paired examples of compounds from distinct chemical series and close structural analogs, we demonstrate that this assay can differentiate drugs with lower DILI risk. The utility of this in vitro transcriptomic approach was also examined using human HEPATOPAC from a single donor, yielding 68% sensitivity and 86% specificity when the aforementioned criteria are applied to the same 93-drug test set. Routine use of the rat model has been adopted with deployment of the human model as warranted on a case-by-case basis. This in vitro transcriptomic signature-based strategy can be used early in drug discovery to derisk DILI potential from chemically reactive metabolites by guiding structure-activity relationship hypotheses and candidate selection.

Application of a Rat Liver Drug Bioactivation Transcriptional Response Assay Early in Drug Development That Informs Chemically Reactive Metabolite Formation and Potential for Drug-induced Liver Injury.

Drug-induced liver injury is a major reason for drug candidate attrition from development, denied commercialization, market withdrawal, and restricted prescribing of pharmaceuticals. The metabolic bioactivation of drugs to chemically reactive metabolites (CRMs) contribute to liver-associated adverse drug reactions in humans that often goes undetected in conventional animal toxicology studies. A challenge for pharmaceutical drug discovery has been reliably selecting drug candidates with a low liability of forming CRM and reduced drug-induced liver injury potential, at projected therapeutic doses, without falsely restricting the development of safe drugs. We have developed an in vivo rat liver transcriptional signature biomarker reflecting the cellular response to drug bioactivation. Measurement of transcriptional activation of integrated nuclear factor erythroid 2-related factor 2 (NRF2)/Kelch-like ECH-associated protein 1 (KEAP1) electrophilic stress, and nuclear factor erythroid 2-related factor 1 (NRF1) proteasomal endoplasmic reticulum (ER) stress responses, is described for discerning estimated clinical doses of drugs with potential for bioactivation-mediated hepatotoxicity. The approach was established using well benchmarked CRM forming test agents from our company. This was subsequently tested using curated lists of commercial drugs and internal compounds, anchored in the clinical experience with human hepatotoxicity, while agnostic to mechanism. Based on results with 116 compounds in short-term rat studies, with consideration of the maximum recommended daily clinical dose, this CRM mechanism-based approach yielded 32% sensitivity and 92% specificity for discriminating safe from hepatotoxic drugs. The approach adds new information for guiding early candidate selection and informs structure activity relationships (SAR) thus enabling lead optimization and mechanistic problem solving. Additional refinement of the model is ongoing. Case examples are provided describing the strengths and limitations of the approach.

Quantitative Transcriptional Biomarkers of Xenobiotic Receptor Activation in Rat Liver for the Early Assessment of Drug Safety Liabilities.

The robust transcriptional plasticity of liver mediated through xenobiotic receptors underlies its ability to respond rapidly and effectively to diverse chemical stressors. Thus, drug-induced gene expression changes in liver serve not only as biomarkers of liver injury, but also as mechanistic sentinels of adaptation in metabolism, detoxification, and tissue protection from chemicals. Modern RNA sequencing methods offer an unmatched opportunity to quantitatively monitor these processes in parallel and to contextualize the spectrum of dose-dependent stress, adaptation, protection, and injury responses induced in liver by drug treatments. Using this approach, we profiled the transcriptional changes in rat liver following daily oral administration of 120 different compounds, many of which are known to be associated with clinical risk for drug-induced liver injury by diverse mechanisms. Clustering, correlation, and linear modeling analyses were used to identify and optimize coexpressed gene signatures modulated by drug treatment. Here, we specifically focused on prioritizing 9 key signatures for their pragmatic utility for routine monitoring in initial rat tolerability studies just prior to entering drug development. These signatures are associated with 5 canonical xenobiotic nuclear receptors (AHR, CAR, PXR, PPARα, ER), 3 mediators of reactive metabolite-mediated stress responses (NRF2, NRF1, P53), and 1 liver response following activation of the innate immune response. Comparing paradigm chemical inducers of each receptor to the other compounds surveyed enabled us to identify sets of optimized gene expression panels and associated scoring algorithms proposed as quantitative mechanistic biomarkers with high sensitivity, specificity, and quantitative accuracy. These findings were further qualified using public datasets, Open TG-GATEs and DrugMatrix, and internal development compounds. With broader collaboration and additional qualification, the quantitative toxicogenomic framework described here could inform candidate selection prior to committing to drug development, as well as complement and provide a deeper understanding of the conventional toxicology study endpoints used later in drug development.

Comparative transcriptomics of choroid plexus in Alzheimer's disease, frontotemporal dementia and Huntington's disease: implications for CSF homeostasis.

BACKGROUND: In Alzheimer's disease, there are striking changes in CSF composition that relate to altered choroid plexus (CP) function. Studying CP tissue gene expression at the blood-cerebrospinal fluid barrier could provide further insight into the epithelial and stromal responses to neurodegenerative disease states. METHODS: Transcriptome-wide Affymetrix microarrays were used to determine disease-related changes in gene expression in human CP. RNA from post-mortem samples of the entire lateral ventricular choroid plexus was extracted from 6 healthy controls (Ctrl), 7 patients with advanced (Braak and Braak stage III-VI) Alzheimer's disease (AD), 4 with frontotemporal dementia (FTD) and 3 with Huntington's disease (HuD). Statistics and agglomerative clustering were accomplished with MathWorks, MatLab; and gene set annotations by comparing input sets to GeneGo ( http://www.genego.com ) and Ingenuity ( http://www.ingenuity.com ) pathway sets. Bonferroni-corrected hypergeometric p-values of < 0.1 were considered a significant overlap between sets. RESULTS: Pronounced differences in gene expression occurred in CP of advanced AD patients vs. Ctrls. Metabolic and immune-related pathways including acute phase response, cytokine, cell adhesion, interferons, and JAK-STAT as well as mTOR were significantly enriched among the genes upregulated. Methionine degradation, claudin-5 and protein translation genes were downregulated. Many gene expression changes in AD patients were observed in FTD and HuD (e.g., claudin-5, tight junction downregulation), but there were significant differences between the disease groups. In AD and HuD (but not FTD), several neuroimmune-modulating interferons were significantly enriched (e.g., in AD: IFI-TM1, IFN-AR1, IFN-AR2, and IFN-GR2). AD-associated expression changes, but not those in HuD and FTD, were enriched for upregulation of VEGF signaling and immune response proteins, e.g., interleukins. HuD and FTD patients distinctively displayed upregulated cadherin-mediated adhesion. CONCLUSIONS: Our transcript data for human CP tissue provides genomic and mechanistic insight for differential expression in AD vs. FTD vs. HuD for stromal as well as epithelial components. These choroidal transcriptome characterizations elucidate immune activation, tissue functional resiliency, and CSF metabolic homeostasis. The BCSFB undergoes harmful, but also important functional and adaptive changes in neurodegenerative diseases; accordingly, the enriched JAK-STAT and mTOR pathways, respectively, likely help the CP in adaptive transcription and epithelial repair and/or replacement when harmed by neurodegeneration pathophysiology. We anticipate that these precise CP translational data will facilitate pharmacologic/transgenic therapies to alleviate dementia.

Transcriptional Profiling of Cholinergic Neurons From Basal Forebrain Identifies Changes in Expression of Genes Between Sleep and Wake.

Study objective: To assess differences in gene expression in cholinergic basal forebrain cells between sleeping and sleep-deprived mice sacrificed at the same time of day. Methods: Tg(ChAT-eGFP)86Gsat mice expressing enhanced green fluorescent protein (eGFP) under control of the choline acetyltransferase (Chat) promoter were utilized to guide laser capture of cholinergic cells in basal forebrain. Messenger RNA expression levels in these cells were profiled using microarrays. Gene expression in eGFP(+) neurons was compared (1) to that in eGFP(-) neurons and to adjacent white matter, (2) between 7:00 am (lights on) and 7:00 pm (lights off), (3) between sleep-deprived and sleeping animals at 0, 3, 6, and 9 hours from lights on. Results: There was a marked enrichment of ChAT and other markers of cholinergic neurons in eGFP(+) cells. Comparison of gene expression in these eGFP(+) neurons between 7:00 am and 7:00 pm revealed expected differences in the expression of clock genes (Arntl2, Per1, Per2, Dbp, Nr1d1) as well as mGluR3. Comparison of expression between spontaneous sleep and sleep-deprived groups sacrificed at the same time of day revealed a number of transcripts (n = 55) that had higher expression in sleep deprivation compared to sleep. Genes upregulated in sleep deprivation predominantly were from the protein folding pathway (25 transcripts, including chaperones). Among 42 transcripts upregulated in sleep was the cold-inducible RNA-binding protein. Conclusions: Cholinergic cell signatures were characterized. Whether the identified genes are changing as a consequence of differences in behavioral state or as part of the molecular regulatory mechanism remains to be determined.

Discovering Biology in Periodic Data through Phase Set Enrichment Analysis (PSEA).

Several tools use prior biological knowledge to interpret gene expression data. However, existing enrichment tools assume that variables are monotonic and incorrectly measure the distance between periodic phases. As a result, these tools are poorly suited for the analysis of the cell cycle, circadian clock, or other periodic systems. Here, we develop Phase Set Enrichment Analysis (PSEA) to incorporate prior knowledge into the analysis of periodic data. PSEA identifies biologically related gene sets showing temporally coordinated expression. Using synthetic gene sets of various sizes generated from von Mises (circular normal) distributions, we benchmarked PSEA alongside existing methods. PSEA offered enhanced sensitivity over a broad range of von Mises distributions and gene set sizes. Importantly, and unlike existing tools, the sensitivity of PSEA is independent of the mean expression phase of the set. We applied PSEA to 4 published datasets. Application of PSEA to the mouse circadian atlas revealed that several pathways, including those regulating immune and cell-cycle function, demonstrate temporal orchestration across multiple tissues. We then applied PSEA to the phase shifts following a restricted feeding paradigm. We found that this perturbation disrupts intraorgan metabolic synchrony in the liver, altering the timing between anabolic and catabolic pathways. Reanalysis of expression data using custom gene sets derived from recent ChIP-seq results revealed circadian transcriptional targets bound exclusively by CLOCK, independently of BMAL1, differ from other exclusive circadian output genes and have well-synchronized phases. Finally, we used PSEA to compare 2 cell-cycle datasets. PSEA increased the apparent biological overlap while also revealing evidence of cell-cycle dysregulation in these cancer cells. To encourage its use by the community, we have implemented PSEA as a Java application. In sum, PSEA offers a powerful new tool to investigate large-scale, periodic data for biological insight.

Effects of Long-Term Odanacatib Treatment on Bone Gene Expression in Ovariectomized Adult Rhesus Monkeys: Differentiation From Alendronate.

Similar efficacy of the cathepsin K inhibitor odanacatib (ODN) and the bisphosphonate alendronate (ALN) in reducing bone turnover markers and increasing bone mineral density in spine and hip were previously demonstrated in ovariectomized (OVX)-monkeys treated for 20 months in prevention mode. Here, we profiled RNA from tibial metaphysis and diaphysis of the same study using Affymetrix microarrays, and selected 204 probe sets (p < 0.001, three-group ANOVA) that were differentially regulated by ODN or ALN versus vehicle. Both drugs produced strikingly different effects on known bone-related genes and pathways at the transcriptional level. Although ALN either reduced or had neutral effects on bone resorption-related genes, ODN significantly increased the expression of osteoclast genes (eg, APC5, TNFRSF11A, CTSK, ITGB3, and CALCR), consistent with previous findings on the effects of this agent in enhancing the number of nonresorbing osteoclasts. Conversely, ALN reduced the expression of known bone formation-related genes (eg, TGFBR1, SPP1, RUNX2, and PTH1R), whereas ODN either increased or had neutral effects on their expression. These differential effects of ODN versus ALN on bone resorption and formation were highly correlative to the changes in bone turnover markers, cathepsin K (Catk) target engagement marker serum C-terminal cross-linked telopeptide (1-CTP) and osteoclast marker tartrate resistant acid phosphatase isoform 5b (TRAP5b) in the same monkeys. Overall, the molecular profiling results are consistent with the known pharmacological actions of these agents on bone remodeling and clearly differentiate the molecular mechanisms of ODN from the bisphosphonates.

Blood-gene expression reveals reduced circadian rhythmicity in individuals resistant to sleep deprivation.

STUDY OBJECTIVES: To address whether changes in gene expression in blood cells with sleep loss are different in individuals resistant and sensitive to sleep deprivation. DESIGN: Blood draws every 4 h during a 3-day study: 24-h normal baseline, 38 h of continuous wakefulness and subsequent recovery sleep, for a total of 19 time-points per subject, with every 2-h psychomotor vigilance task (PVT) assessment when awake. SETTING: Sleep laboratory. PARTICIPANTS: Fourteen subjects who were previously identified as behaviorally resistant (n = 7) or sensitive (n = 7) to sleep deprivation by PVT. INTERVENTION: Thirty-eight hours of continuous wakefulness. MEASUREMENTS AND RESULTS: We found 4,481 unique genes with a significant 24-h diurnal rhythm during a normal sleep-wake cycle in blood (false discovery rate [FDR] < 5%). Biological pathways were enriched for biosynthetic processes during sleep. After accounting for circadian effects, two genes (SREBF1 and CPT1A, both involved in lipid metabolism) exhibited small, but significant, linear changes in expression with the duration of sleep deprivation (FDR < 5%). The main change with sleep deprivation was a reduction in the amplitude of the diurnal rhythm of expression of normally cycling probe sets. This reduction was noticeably higher in behaviorally resistant subjects than sensitive subjects, at any given P value. Furthermore, blood cell type enrichment analysis showed that the expression pattern difference between sensitive and resistant subjects is mainly found in cells of myeloid origin, such as monocytes. CONCLUSION: Individual differences in behavioral effects of sleep deprivation are associated with differences in diurnal amplitude of gene expression for genes that show circadian rhythmicity.

Integrated systems approach identifies genetic nodes and networks in late-onset Alzheimer's disease.

The genetics of complex disease produce alterations in the molecular interactions of cellular pathways whose collective effect may become clear through the organized structure of molecular networks. To characterize molecular systems associated with late-onset Alzheimer's disease (LOAD), we constructed gene-regulatory networks in 1,647 postmortem brain tissues from LOAD patients and nondemented subjects, and we demonstrate that LOAD reconfigures specific portions of the molecular interaction structure. Through an integrative network-based approach, we rank-ordered these network structures for relevance to LOAD pathology, highlighting an immune- and microglia-specific module that is dominated by genes involved in pathogen phagocytosis, contains TYROBP as a key regulator, and is upregulated in LOAD. Mouse microglia cells overexpressing intact or truncated TYROBP revealed expression changes that significantly overlapped the human brain TYROBP network. Thus the causal network structure is a useful predictor of response to gene perturbations and presents a framework to test models of disease mechanisms underlying LOAD.

Transcriptional architecture of the primate neocortex.

Genome-wide transcriptional profiling was used to characterize the molecular underpinnings of neocortical organization in rhesus macaque, including cortical areal specialization and laminar cell-type diversity. Microarray analysis of individual cortical layers across sensorimotor and association cortices identified robust and specific molecular signatures for individual cortical layers and areas, prominently involving genes associated with specialized neuronal function. Overall, transcriptome-based relationships were related to spatial proximity, being strongest between neighboring cortical areas and between proximal layers. Primary visual cortex (V1) displayed the most distinctive gene expression compared to other cortical regions in rhesus and human, both in the specialized layer 4 as well as other layers. Laminar patterns were more similar between macaque and human compared to mouse, as was the unique V1 profile that was not observed in mouse. These data provide a unique resource detailing neocortical transcription patterns in a nonhuman primate with great similarity in gene expression to human.

Molecular insights into the pathogenesis of Alzheimer's disease and its relationship to normal aging.

Alzheimer's disease (AD) is a complex neurodegenerative disorder that diverges from the process of normal brain aging by unknown mechanisms. We analyzed the global structure of age- and disease-dependent gene expression patterns in three regions from more than 600 brains. Gene expression variation could be almost completely explained by four transcriptional biomarkers that we named BioAge (biological age), Alz (Alzheimer), Inflame (inflammation), and NdStress (neurodegenerative stress). BioAge captures the first principal component of variation and includes genes statistically associated with neuronal loss, glial activation, and lipid metabolism. Normally BioAge increases with chronological age, but in AD it is prematurely expressed as if some of the subjects were 140 years old. A component of BioAge, Lipa, contains the AD risk factor APOE and reflects an apparent early disturbance in lipid metabolism. The rate of biological aging in AD patients, which cannot be explained by BioAge, is associated instead with NdStress, which includes genes related to protein folding and metabolism. Inflame, comprised of inflammatory cytokines and microglial genes, is broadly activated and appears early in the disease process. In contrast, the disease-specific biomarker Alz was selectively present only in the affected areas of the AD brain, appears later in pathogenesis, and is enriched in genes associated with the signaling and cell adhesion changes during the epithelial to mesenchymal (EMT) transition. Together these biomarkers provide detailed description of the aging process and its contribution to Alzheimer's disease progression.

Reconstruction and stability of secondary structure elements in the context of protein structure prediction.

Efficient and accurate reconstruction of secondary structure elements in the context of protein structure prediction is the major focus of this work. We present a novel approach capable of reconstructing alpha-helices and beta-sheets in atomic detail. The method is based on Metropolis Monte Carlo simulations in a force field of empirical potentials that are designed to stabilize secondary structure elements in room-temperature simulations. Particular attention is paid to lateral side-chain interactions in beta-sheets and between the turns of alpha-helices, as well as backbone hydrogen bonding. The force constants are optimized using contrastive divergence, a novel machine learning technique, from a data set of known structures. Using this approach, we demonstrate the applicability of the framework to the problem of reconstructing the overall protein fold for a number of commonly studied small proteins, based on only predicted secondary structure and contact map. For protein G and chymotrypsin inhibitor 2, we are able to reconstruct the secondary structure elements in atomic detail and the overall protein folds with a root mean-square deviation of <10 A. For cold-shock protein and the SH3 domain, we accurately reproduce the secondary structure elements and the topology of the 5-stranded beta-sheets, but not the barrel structure. The importance of high-quality secondary structure and contact map prediction is discussed.

CRANKITE: A fast polypeptide backbone conformation sampler.

BACKGROUND: CRANKITE is a suite of programs for simulating backbone conformations of polypeptides and proteins. The core of the suite is an efficient Metropolis Monte Carlo sampler of backbone conformations in continuous three-dimensional space in atomic details. METHODS: In contrast to other programs relying on local Metropolis moves in the space of dihedral angles, our sampler utilizes local crankshaft rotations of rigid peptide bonds in Cartesian space. RESULTS: The sampler allows fast simulation and analysis of secondary structure formation and conformational changes for proteins of average length.

Learning about protein hydrogen bonding by minimizing contrastive divergence.

Defining the strength and geometry of hydrogen bonds in protein structures has been a challenging task since early days of structural biology. In this article, we apply a novel statistical machine learning technique, known as contrastive divergence, to efficiently estimate both the hydrogen bond strength and the geometric characteristics of strong interpeptide backbone hydrogen bonds, from a dataset of structures representing a variety of different protein folds. Despite the simplifying assumptions of the interatomic energy terms used, we determine the strength of these hydrogen bonds to be between 1.1 and 1.5 kcal/mol, in good agreement with earlier experimental estimates. The geometry of these strong backbone hydrogen bonds features an almost linear arrangement of all four atoms involved in hydrogen bond formation. We estimate that about a quarter of all hydrogen bond donors and acceptors participate in these strong interpeptide hydrogen bonds.

Exhaustive Metropolis Monte Carlo sampling and analysis of polyalanine conformations adopted under the influence of hydrogen bonds.

We propose a novel Metropolis Monte Carlo procedure for protein modeling and analyze the influence of hydrogen bonding on the distribution of polyalanine conformations. We use an atomistic model of the polyalanine chain with rigid and planar polypeptide bonds, and elastic alpha carbon valence geometry. We adopt a simplified energy function in which only hard-sphere repulsion and hydrogen bonding interactions between the atoms are considered. Our Metropolis Monte Carlo procedure utilizes local crankshaft moves and is combined with parallel tempering to exhaustively sample the conformations of 16-mer polyalanine. We confirm that Flory's isolated-pair hypothesis (the steric independence between the dihedral angles of individual amino acids) does not hold true in long polypeptide chains. In addition to 3(10)- and alpha-helices, we identify a kink stabilized by 2 hydrogen bonds with a shared acceptor as a common structural motif. Varying the strength of hydrogen bonds, we induce the helix-coil transition in the model polypeptide chain. We compare the propensities for various hydrogen bonding patterns and determine the degree of cooperativity of hydrogen bond formation in terms of the Hill coefficient. The observed helix-coil transition is also quantified according to Zimm-Bragg theory.

Modeling HIV-1 integrase complexes based on their hydrodynamic properties.

We present a model structure of a candidate tetramer for HIV-1 integrase. The model was built in three steps using data from fluorescence anisotropy, structures of the individual integrase domains, cross-linking data, and other biochemical data. First, the structure of the full-length integrase monomer was modeled using the individual domain structures and the hydrodynamic properties of the full-length protein that were recently measured by fluorescence depolarization. We calculated the rotational correlation times for different arrangements of three integrase domains, revealing that only structures with close proximity among the domains satisfied the experimental data. The orientations of the domains were constrained by iterative tests against the data on cross-linking and footprinting in integrase-DNA complexes. Second, the structure of an integrase dimer was obtained by joining the model monomers in accordance with the available dimeric crystal structures of the catalytic core. The hydrodynamic properties of the dimer were in agreement with the experimental values. Third, the active sites of the two model dimers were placed in agreement with the spacing between the sites of integration on target DNA as well as the integrase-DNA cross-linking data, resulting in twofold symmetry of a tetrameric complex. The model is consistent with the experimental data indicating that the F185K substitution, which is found in the model at a tetramerization interface, selectively disrupts correct complex formation in vitro and HIV replication in vivo. Our model of the integrase tetramer bound to DNA may help to design anti-integrase inhibitors.