Stochastic models and numerical algorithms for a class of regulatory gene networks.

Regulatory gene networks contain generic modules, like those involving feedback loops, which are essential for the regulation of many biological functions (Guido et al. in Nature 439:856-860, 2006). We consider a class of self-regulated genes which are the building blocks of many regulatory gene networks, and study the steady-state distribution of the associated Gillespie algorithm by providing efficient numerical algorithms. We also study a regulatory gene network of interest in gene therapy, using mean-field models with time delays. Convergence of the related time-nonhomogeneous Markov chain is established for a class of linear catalytic networks with feedback loops.

Immunization with HIV Gag targeted to dendritic cells followed by recombinant New York vaccinia virus induces robust T-cell immunity in nonhuman primates.

Protein vaccines, if rendered immunogenic, would facilitate vaccine development against HIV and other pathogens. We compared in nonhuman primates (NHPs) immune responses to HIV Gag p24 within 3G9 antibody to DEC205 ("DEC-HIV Gag p24"), an uptake receptor on dendritic cells, to nontargeted protein, with or without poly ICLC, a synthetic double stranded RNA, as adjuvant. Priming s.c. with 60 μg of both HIV Gag p24 vaccines elicited potent CD4(+) T cells secreting IL-2, IFN-γ, and TNF-α, which also proliferated. The responses increased with each of three immunizations and recognized multiple Gag peptides. DEC-HIV Gag p24 showed better cross-priming for CD8(+) T cells, whereas the avidity of anti-Gag antibodies was ∼10-fold higher with nontargeted Gag 24 protein. For both protein vaccines, poly ICLC was essential for T- and B-cell immunity. To determine whether adaptive responses could be further enhanced, animals were boosted with New York vaccinia virus (NYVAC)-HIV Gag/Pol/Nef. Gag-specific CD4(+) and CD8(+) T-cell responses increased markedly after priming with both protein vaccines and poly ICLC. These data reveal qualitative differences in antibody and T-cell responses to DEC-HIV Gag p24 and Gag p24 protein and show that prime boost with protein and adjuvant followed by NYVAC elicits potent cellular immunity.

Dosage optimization of treatments using population pharmacokinetic modeling and simulation.

Pharmacokinetic variability in drug levels represent for some drugs a major determinant of treatment success, since sub-therapeutic concentrations might lead to toxic reactions, treatment discontinuation or inefficacy. This is true for most antiretroviral drugs, which exhibit high inter-patient variability in their pharmacokinetics that has been partially explained by some genetic and non-genetic factors. The population pharmacokinetic approach represents a very useful tool for the description of the dose-concentration relationship, the quantification of variability in the target population of patients and the identification of influencing factors. It can thus be used to make predictions and dosage adjustment optimization based on Bayesian therapeutic drug monitoring (TDM). This approach has been used to characterize the pharmacokinetics of nevirapine (NVP) in 137 HIV-positive patients followed within the frame of a TDM program. Among tested covariates, body weight, co-administration of a cytochrome (CYP) 3A4 inducer or boosted atazanavir as well as elevated aspartate transaminases showed an effect on NVP elimination. In addition, genetic polymorphism in the CYP2B6 was associated with reduced NVP clearance. Altogether, these factors could explain 26% in NVP variability. Model-based simulations were used to compare the adequacy of different dosage regimens in relation to the therapeutic target associated with treatment efficacy. In conclusion, the population approach is very useful to characterize the pharmacokinetic profile of drugs in a population of interest. The quantification and the identification of the sources of variability is a rational approach to making optimal dosage decision for certain drugs administered chronically.

Large-scale clustering through functional embedding

We present a new framework for large-scale data clustering. The main idea is to modify functional dimensionality reduction techniques to directly optimize over discrete labels using stochastic gradient descent. Compared to methods like spectral clustering our approach solves a single optimization problem, rather than an ad-hoc two-stage optimization approach, does not require a matrix inversion, can easily encode prior knowledge in the set of implementable functions, and does not have an ?out-of-sample? problem. Experimental results on both artificial and real-world datasets show the usefulness of our approach.

Optimization of HS-SPME/GC-MS analysis and its use in the profiling of illicit ecstasy tablets (Part 1)

A headspace solid-phase microextraction procedure (HS-SPME) was developed for the profiling of traces present in 3,4-methylenedioxymethylampethamine (MDMA). Traces were first extracted using HS-SPME and then analyzed by gas chromatography-mass spectroscopy (GC-MS). The HS-SPME conditions were optimized using varying conditions. Optimal results were obtained when 40 mg of crushed MDMA sample was heated at 80 °C for 15 min, followed by extraction at 80 °C for 15 min with a polydimethylsiloxane/divinylbenzene coated fibre. A total of 31 compounds were identified as traces related to MDMA synthesis, namely precursors, intermediates or by-products. In addition some fatty acids used as tabletting materials and caffeine used as adulterant, were also detected. The use of a restricted set of 10 target compounds was also proposed for developing a screening tool for clustering samples having close profile. 114 seizures were analyzed using an SPME auto-sampler (MultiPurpose Samples MPS2), purchased from Gerstel GMBH & Co. (Germany), and coupled to GC-MS. The data was handled using various pre-treatment methods, followed by the study of similarities between sample pairs based on the Pearson correlation. The results show that HS-SPME, coupled with the suitable statistical method is a powerful tool for distinguishing specimens coming from the same seizure and specimens coming from different seizures. This information can be used by law enforcement personnel to visualize the ecstasy distribution network as well as the clandestine tablet manufacturing.

Detailed analysis and follow-up studies of a high-throughput screening for indoleamine 2,3-dioxygenase 1 (IDO1) inhibitors.

Indoleamine 2,3-dioxygenase 1 (IDO1) is a key regulator of immune responses and therefore an important therapeutic target for the treatment of diseases that involve pathological immune escape, such as cancer. Here, we describe a robust and sensitive high-throughput screen (HTS) for IDO1 inhibitors using the Prestwick Chemical Library of 1200 FDA-approved drugs and the Maybridge HitFinder Collection of 14,000 small molecules. Of the 60 hits selected for follow-up studies, 14 displayed IC50 values below 20 μM under the secondary assay conditions, and 4 showed an activity in cellular tests. In view of the high attrition rate we used both experimental and computational techniques to identify and to characterize compounds inhibiting IDO1 through unspecific inhibition mechanisms such as chemical reactivity, redox cycling, or aggregation. One specific IDO1 inhibitor scaffold, the imidazole antifungal agents, was chosen for rational structure-based lead optimization, which led to more soluble and smaller compounds with micromolar activity.

Quantitative integration of high-resolution hydrogeophysical data through Monte-Carlo-type conditional simulations

Geophysical techniques can help to bridge the inherent gap with regard to spatial resolution and the range of coverage that plagues classical hydrological methods. This has lead to the emergence of the new and rapidly growing field of hydrogeophysics. Given the differing sensitivities of various geophysical techniques to hydrologically relevant parameters and their inherent trade-off between resolution and range the fundamental usefulness of multi-method hydrogeophysical surveys for reducing uncertainties in data analysis and interpretation is widely accepted. A major challenge arising from such endeavors is the quantitative integration of the resulting vast and diverse database in order to obtain a unified model of the probed subsurface region that is internally consistent with all available data. To address this problem, we have developed a strategy towards hydrogeophysical data integration based on Monte-Carlo-type conditional stochastic simulation that we consider to be particularly suitable for local-scale studies characterized by high-resolution and high-quality datasets. Monte-Carlo-based optimization techniques are flexible and versatile, allow for accounting for a wide variety of data and constraints of differing resolution and hardness and thus have the potential of providing, in a geostatistical sense, highly detailed and realistic models of the pertinent target parameter distributions. Compared to more conventional approaches of this kind, our approach provides significant advancements in the way that the larger-scale deterministic information resolved by the hydrogeophysical data can be accounted for, which represents an inherently problematic, and as of yet unresolved, aspect of Monte-Carlo-type conditional simulation techniques. We present the results of applying our algorithm to the integration of porosity log and tomographic crosshole georadar data to generate stochastic realizations of the local-scale porosity structure. Our procedure is first tested on pertinent synthetic data and then applied to corresponding field data collected at the Boise Hydrogeophysical Research Site near Boise, Idaho, USA.

An allele-specific, stochastic gene expression process controls the expression of multiple Ly49 family genes and generates a diverse, MHC-specific NK cell receptor repertoire.

Mouse NK cells express MHC class I-specific inhibitory Ly49 receptors. Since these receptors display distinct ligand specificities and are clonally distributed, their expression generates a diverse NK cell receptor repertoire specific for MHC class I molecules. We have previously found that the Dd (or Dk)-specific Ly49A receptor is usually expressed from a single allele. However, a small fraction of short-term NK cell clones expressed both Ly49A alleles, suggesting that the two Ly49A alleles are independently and randomly expressed. Here we show that the genes for two additional Ly49 receptors (Ly49C and Ly49G2) are also expressed in a (predominantly) mono-allelic fashion. Since single NK cells can co-express multiple Ly49 receptors, we also investigated whether mono-allelic expression from within the tightly linked Ly49 gene cluster is coordinate or independent. Our clonal analysis suggests that the expression of alleles of distinct Ly49 genes is not coordinate. Thus Ly49 alleles are apparently independently and randomly chosen for stable expression, a process that directly restricts the number of Ly49 receptors expressed per single NK cell. We propose that the Ly49 receptor repertoire specific for MHC class I is generated by an allele-specific, stochastic gene expression process that acts on the entire Ly49 gene cluster.

Stochastic inversion of tracer test and electrical geophysical data to estimate hydraulic conductivities.

Quantifying the spatial configuration of hydraulic conductivity (K) in heterogeneous geological environments is essential for accurate predictions of contaminant transport, but is difficult because of the inherent limitations in resolution and coverage associated with traditional hydrological measurements. To address this issue, we consider crosshole and surface-based electrical resistivity geophysical measurements, collected in time during a saline tracer experiment. We use a Bayesian Markov-chain-Monte-Carlo (McMC) methodology to jointly invert the dynamic resistivity data, together with borehole tracer concentration data, to generate multiple posterior realizations of K that are consistent with all available information. We do this within a coupled inversion framework, whereby the geophysical and hydrological forward models are linked through an uncertain relationship between electrical resistivity and concentration. To minimize computational expense, a facies-based subsurface parameterization is developed. The Bayesian-McMC methodology allows us to explore the potential benefits of including the geophysical data into the inverse problem by examining their effect on our ability to identify fast flowpaths in the subsurface, and their impact on hydrological prediction uncertainty. Using a complex, geostatistically generated, two-dimensional numerical example representative of a fluvial environment, we demonstrate that flow model calibration is improved and prediction error is decreased when the electrical resistivity data are included. The worth of the geophysical data is found to be greatest for long spatial correlation lengths of subsurface heterogeneity with respect to wellbore separation, where flow and transport are largely controlled by highly connected flowpaths.

Robust parametric indirect estimates of the expected cost of a hospital stay with covariates and censored data.

We consider the problem of estimating the mean hospital cost of stays of a class of patients (e.g., a diagnosis-related group) as a function of patient characteristics. The statistical analysis is complicated by the asymmetry of the cost distribution, the possibility of censoring on the cost variable, and the occurrence of outliers. These problems have often been treated separately in the literature, and a method offering a joint solution to all of them is still missing. Indirect procedures have been proposed, combining an estimate of the duration distribution with an estimate of the conditional cost for a given duration. We propose a parametric version of this approach, allowing for asymmetry and censoring in the cost distribution and providing a mean cost estimator that is robust in the presence of extreme values. In addition, the new method takes covariate information into account.

The branch-site test of positive selection is surprisingly robust but lacks power under synonymous substitution saturation and variation in GC

Positive selection is widely estimated from protein coding sequence alignments by the nonsynonymous-to-synonymous ratio omega. Increasingly elaborate codon models are used in a likelihood framework for this estimation. Although there is widespread concern about the robustness of the estimation of the omega ratio, more efforts are needed to estimate this robustness, especially in the context of complex models. Here, we focused on the branch-site codon model. We investigated its robustness on a large set of simulated data. First, we investigated the impact of sequence divergence. We found evidence of underestimation of the synonymous substitution rate for values as small as 0.5, with a slight increase in false positives for the branch-site test. When dS increases further, underestimation of dS is worse, but false positives decrease. Interestingly, the detection of true positives follows a similar distribution, with a maximum for intermediary values of dS. Thus, high dS is more of a concern for a loss of power (false negatives) than for false positives of the test. Second, we investigated the impact of GC content. We showed that there is no significant difference of false positives between high GC (up to similar to 80%) and low GC (similar to 30%) genes. Moreover, neither shifts of GC content on a specific branch nor major shifts in GC along the gene sequence generate many false positives. Our results confirm that the branch-site is a very conservative test.

A novel HIV vaccine adjuvanted by IC31 induces robust and persistent humoral and cellular immunity.

The HIV vaccine strategy that, to date, generated immune protection consisted of a prime-boost regimen using a canarypox vector and an HIV envelope protein with alum, as shown in the RV144 trial. Since the efficacy was weak, and previous HIV vaccine trials designed to generate antibody responses failed, we hypothesized that generation of T cell responses would result in improved protection. Thus, we tested the immunogenicity of a similar envelope-based vaccine using a mouse model, with two modifications: a clade C CN54gp140 HIV envelope protein was adjuvanted by the TLR9 agonist IC31®, and the viral vector was the vaccinia strain NYVAC-CN54 expressing HIV envelope gp120. The use of IC31® facilitated immunoglobulin isotype switching, leading to the production of Env-specific IgG2a, as compared to protein with alum alone. Boosting with NYVAC-CN54 resulted in the generation of more robust Th1 T cell responses. Moreover, gp140 prime with IC31® and alum followed by NYVAC-CN54 boost resulted in the formation and persistence of central and effector memory populations in the spleen and an effector memory population in the gut. Our data suggest that this regimen is promising and could improve the protection rate by eliciting strong and long-lasting humoral and cellular immune responses.

Robust vaccine-elicited cellular immune responses in breast milk following systemic Simian Immunodeficiency Virus DNA prime and live virus vector boost vaccination of lactating rhesus monkeys.

Breast milk transmission of HIV remains an important mode of infant HIV acquisition. Enhancement of mucosal HIV-specific immune responses in milk of HIV-infected mothers through vaccination may reduce milk virus load or protect against virus transmission in the infant gastrointestinal tract. However, the ability of HIV/SIV strategies to induce virus-specific immune responses in milk has not been studied. In this study, five uninfected, hormone-induced lactating, Mamu A*01(+) female rhesus monkey were systemically primed and boosted with rDNA and the attenuated poxvirus vector, NYVAC, containing the SIVmac239 gag-pol and envelope genes. The monkeys were boosted a second time with a recombinant Adenovirus serotype 5 vector containing matching immunogens. The vaccine-elicited immunodominant epitope-specific CD8(+) T lymphocyte response in milk was of similar or greater magnitude than that in blood and the vaginal tract but higher than that in the colon. Furthermore, the vaccine-elicited SIV Gag-specific CD4(+) and CD8(+) T lymphocyte polyfunctional cytokine responses were more robust in milk than in blood after each virus vector boost. Finally, SIV envelope-specific IgG responses were detected in milk of all monkeys after vaccination, whereas an SIV envelope-specific IgA response was only detected in one vaccinated monkey. Importantly, only limited and transient increases in the proportion of activated or CCR5-expressing CD4(+) T lymphocytes in milk occurred after vaccination. Therefore, systemic DNA prime and virus vector boost of lactating rhesus monkeys elicits potent virus-specific cellular and humoral immune responses in milk and may warrant further investigation as a strategy to impede breast milk transmission of HIV.