A Spatial Modeling Approach to Identify Potential Butternut Restoration Sites in Mammoth Cave National Park

Incorporation of disease resistance is nearly complete for several important North American hardwood species threatened by exotic fungal diseases. The next important step toward species restoration would be to develop reliable tools to delineate ideal restoration sites on a landscape scale. We integrated spatial modeling and remote sensing techniques to delineate potential restoration sites for Butternut (Juglans cinerea L.) trees, a hardwood species being decimated by an exotic fungus, in Mammoth Cave National Park (MCNP), Kentucky. We first developed a multivariate habitat model to determine optimum Butternut habitats within MCNP. Habitat characteristics of 54 known Butternut locations were used in combination with eight topographic and land use data layers to calculate an index of habitat suitability based on Mahalanobis distance (D²). We used a bootstrapping technique to test the reliability of model predictions. Based on a threshold value for the D² statistic, 75.9% of the Butternut locations were correctly classified, indicating that the habitat model performed well. Because Butternut seedlings require extensive amounts of sunlight to become established, we used canopy cover data to refine our delineation of favorable areas for Butternut restoration. Areas with the most favorable conditions to establish Butternut seedlings were limited to 291.6 ha. Our study provides a useful reference on the amount and location of favorable Butternut habitat in MCNP and can be used to identify priority areas for future Butternut restoration. Given the availability of relevant habitat layers and accurate location records, our approach can be applied to other tree species and areas.     

A Model-Based Approach to Assess Epidemic Risk

Statistics in Biosciences - We study how international flights can facilitate the spread of an epidemic to a worldwide scale. We combine an infrastructure network of flight connections with a...     

A Mutant Library Approach to Identify Improved Meningococcal Factor H Binding Protein Vaccine Antigens

Factor H binding protein (FHbp) is a virulence factor used by meningococci to evade the host complement system. FHbp elicits bactericidal antibodies in humans and is part of two recently licensed vaccines. Using human complement Factor H (FH) transgenic mice, we previously showed that binding of FH decreased the protective antibody responses to FHbp vaccination. Therefore, in the present study we devised a library-based method to identify mutant FHbp antigens with very low binding of FH. Using an FHbp sequence variant in one of the two licensed vaccines, we displayed an error-prone PCR mutant FHbp library on the surface of Escherichia coli. We used fluorescence-activated cell sorting to isolate FHbp mutants with very low binding of human FH and preserved binding of control anti-FHbp monoclonal antibodies. We sequenced the gene encoding FHbp from selected clones and introduced the mutations into a soluble FHbp construct. Using this approach, we identified several new mutant FHbp vaccine antigens that had very low binding of FH as measured by ELISA and surface plasmon resonance. The new mutant FHbp antigens elicited protective antibody responses in human FH transgenic mice that were up to 20-fold higher than those elicited by the wild-type FHbp antigen. This approach offers the potential to discover mutant antigens that might not be predictable even with protein structural information and potentially can be applied to other microbial vaccine antigens that bind host proteins.     

A Model-Based Approach for Making Ecological Inference from Distance Sampling Data

Summary .  We consider a fully model-based approach for the analysis of distance sampling data. Distance sampling has been widely used to estimate abundance (or density) of animals or plants in a spatially explicit study area. There is, however, no readily available method of making statistical inference on the relationships between abundance and environmental covariates. Spatial Poisson process likelihoods can be used to simultaneously estimate detection and intensity parameters by modeling distance sampling data as a thinned spatial point process. A model-based spatial approach to distance sampling data has three main benefits: it allows complex and opportunistic transect designs to be employed, it allows estimation of abundance in small subregions, and it provides a framework to assess the effects of habitat or experimental manipulation on density. We demonstrate the model-based methodology with a small simulation study and analysis of the Dubbo weed data set. In addition, a simple ad hoc method for handling overdispersion is also proposed. The simulation study showed that the model-based approach compared favorably to conventional distance sampling methods for abundance estimation. In addition, the overdispersion correction performed adequately when the number of transects was high. Analysis of the Dubbo data set indicated a transect effect on abundance via Akaike's information criterion model selection. Further goodness-of-fit analysis, however, indicated some potential confounding of intensity with the detection function.     

A Model-Based Approach for Identifying Signatures of Ancient Balancing Selection in Genetic Data

While much effort has focused on detecting positive and negative directional selection in the human genome, relatively little work has been devoted to balancing selection. This lack of attention is likely due to the paucity of sophisticated methods for identifying sites under balancing selection. Here we develop two composite likelihood ratio tests for detecting balancing selection. Using simulations, we show that these methods outperform competing methods under a variety of assumptions and demographic models. We apply the new methods to whole-genome human data, and find a number of previously-identified loci with strong evidence of balancing selection, including several HLA genes. Additionally, we find evidence for many novel candidates, the strongest of which is FANK1, an imprinted gene that suppresses apoptosis, is expressed during meiosis in males, and displays marginal signs of segregation distortion. We hypothesize that balancing selection acts on this locus to stabilize the segregation distortion and negative fitness effects of the distorter allele. Thus, our methods are able to reproduce many previously-hypothesized signals of balancing selection, as well as discover novel interesting candidates.     

A Model-Based Approach for Species Abundance Quantification Based on Shotgun Metagenomic Data

The human microbiome, which includes the collective microbes residing in or on the human body, has a profound influence on the human health. DNA sequencing technology has made the large-scale human microbiome studies possible by using shotgun metagenomic sequencing. One important aspect of data analysis of such metagenomic data is to quantify the bacterial abundances based on the metagenomic sequencing data. Existing methods almost always quantify such abundances one sample at a time, which ignore certain systematic differences in read coverage along the genomes due to GC contents, copy number variation and the bacterial origin of replication. In order to account for such differences in read counts, we propose a multi-sample Poisson model to quantify microbial abundances based on read counts that are assigned to species-specific taxonomic markers. Our model takes into account the marker-specific effects when normalizing the sequencing count data in order to obtain more accurate quantification of the species abundances. Compared to currently available methods on simulated data and real data sets, our method has demonstrated an improved accuracy in bacterial abundance quantification, which leads to more biologically interesting results from downstream data analysis.     

A Statistical Approach to Identify Ancient Template DNA

One of the key problems in the study of ancient DNA is that of authenticating sequences obtained from PCR amplifications of highly degraded samples. Contamination of ancient samples and postmortem damage to endogenous DNA templates are the major obstacles facing researchers in this task. In particular, the authentication of sequences obtained from ancient human remains is thought by many to be rather challenging. We propose a novel approach, based on the c statistic, that can be employed to help identify the sequence motif of an endogenous template, based on a sample of sequences that reflect the nucleotide composition of individual template molecules obtained from ancient tissues (such as cloned products from a PCR amplification). The c statistic exploits as information the most common form of postmortem damage observed among clone sequences in ancient DNA studies, namely, lesion-induced substitutions caused by cytosine deamination events. Analyses of simulated sets of templates with miscoding lesions and real sets of clone sequences from the literature indicate that the c-based approach is highly effective in identifying endogenous sequence motifs, even when they are not present among the sampled clones. The proposed approach is likely to be of general use to researchers working with DNA from ancient tissues, particularly from human remains, where authentication of results has been most challenging. [Reviewing Editor: Dr. Magnus Nordborg]     

Melatonin Binding Sites

The distribution and characterization of specific melatonin binding sites were studied using 125I-melatonin. Autoradiography revealed only three sites of specific melatonin binding in brain: the suprachiasmatic nuclei, the median eminence, and the small part of choroid plexus at the caudal end of the fourth ventricle. Two other sites were detected outside the CNS: the anterior pituitary and the retina. The specific binding of 125I-melatonin was saturable and reversible. The dissociation constant (KD) of the binding sites was 60 pM. The concentration of the binding sites (Bmax) in the median eminence was 26 fmol/mg protein, and in the pituitary 3 fmol/mg protein. Specificity of the binding sites was tested by displacement of 125I-melatonin. The order of potency--melatonin much less than N-acetyl-5-hydroxytryptamine less than 5-methoxytryptamine much less than 5-hydroxytryptamine = 3,4-dihydroxyphenylethylamine = noradrenaline--shows high specificity of the binding sites for melatonin.     

A Model-Based Approach to Detection Limits in Studying Environmental Exposures and Human Fecundity

Statistics in Biosciences - Human exposure to persistent environmental pollutants often results in concentrations with a range of values below the laboratory detection limits. Growing evidence...     

Binding of Aminoalkylindoles to Noncannabinoid Binding Sites in NG108-15 Cells

1. Aminoalkylindoles, typified by WIN 55212-2, bind to G protein-coupled cannabinoid receptors in brain. Although cannabinoids inhibit adenylyl cyclase in NG108-15 neuroblastoma × glioma hybrid cells, cannabinoid receptor binding in these cells has not been described previously. This study compares pharamcological characteristics of [³H]WIN 55212-2 binding sites in rat cerebellar membranes and in NG108-15 membranes.2. Although the K _D of specifid [³H]WIN 55212-2 binding was similar in brain and NG108-15 membranes, the B _max was 10 times lower in NG108-15 than in cerebellar membranes. In both brain and NG108-15 membranes, aminoalkylindole analogues were relatively potent in displacing [³H]WIN 55212-2 binding.However, IC₅₀ values for more traditional cannabinoids were significantly higher in NG108-15 membranes than in brain, e.g., the K _i values for CP55,940 were1.2nM in brain and >5000nM in NG108-15 membranes. Moreover, sodium and GTP--S decreased [³H]WIN 55212-2 binding in brain but not in NG108-15membranes.3. These data suggest that WIN 55212-2 does not label traditional cannabinoid receptors in NG108-15 cells and that these novel aminoalkylindolebinding sites are not coupled to G proteins.     

MPI/FT: A Model-Based Approach to Low-Overhead Fault Tolerant Message-Passing Middleware

Fault tolerance in parallel systems has traditionally been achieved through a combination of redundancy and checkpointing methods. This notion has also been extended to message-passing systems with user-transparent process checkpointing and message logging. Furthermore, studies of multiple types of rollback and recovery have been reported in literature, ranging from communication-induced checkpointing to pessimistic and synchronous solutions. However, many of these solutions incorporate high overhead because of their inability to utilize application level information.This paper describes the design and implementation of MPI/FT, a high-performance MPI-1.2 implementation enhanced with low-overhead functionality to detect and recover from process failures. The strategy behind MPI/FT is that fault tolerance in message-passing middleware can be optimized based on an application's execution model derived from its communication topology and parallel programming semantics. MPI/FT exploits the specific characteristics of two parallel application execution models in order to optimize performance. MPI/FT also introduces the self-checking thread that monitors the functioning of the middleware itself. User aware checkpointing and user-assisted recovery are compatible with MPI/FT and complement the techniques used here.This paper offers a classification of MPI applications for fault tolerant MPI purposes and MPI/FT implementation discussed here provides different middleware versions specifically tailored to each of the two models studied in detail. The interplay of various parameters affecting the cost of fault tolerance is investigated. Experimental results demonstrate that the approach used to design and implement MPI/FT results in a low-overhead MPI-based fault tolerant communication middleware implementation.     

A Bayesian Framework to Identify Methylcytosines from High-Throughput Bisulfite Sequencing Data

High-throughput bisulfite sequencing technologies have provided a comprehensive and well-fitted way to investigate DNA methylation at single-base resolution. However, there are substantial bioinformatic challenges to distinguish precisely methylcytosines from unconverted cytosines based on bisulfite sequencing data. The challenges arise, at least in part, from cell heterozygosis caused by multicellular sequencing and the still limited number of statistical methods that are available for methylcytosine calling based on bisulfite sequencing data. Here, we present an algorithm, termed Bycom, a new Bayesian model that can perform methylcytosine calling with high accuracy. Bycom considers cell heterozygosis along with sequencing errors and bisulfite conversion efficiency to improve calling accuracy. Bycom performance was compared with the performance of Lister, the method most widely used to identify methylcytosines from bisulfite sequencing data. The results showed that the performance of Bycom was better than that of Lister for data with high methylation levels. Bycom also showed higher sensitivity and specificity for low methylation level samples (<1%) than Lister. A validation experiment based on reduced representation bisulfite sequencing data suggested that Bycom had a false positive rate of about 4% while maintaining an accuracy of close to 94%. This study demonstrated that Bycom had a low false calling rate at any methylation level and accurate methylcytosine calling at high methylation levels. Bycom will contribute significantly to studies aimed at recalibrating the methylation level of genomic regions based on the presence of methylcytosines.     

A Chemical Screening Approach to Identify Novel Key Mediators of Erythroid Enucleation

Erythroid enucleation is critical for terminal differentiation of red blood cells, and involves extrusion of the nucleus by orthochromatic erythroblasts to produce reticulocytes. Due to the difficulty of synchronizing erythroblasts, the molecular mechanisms underlying the enucleation process remain poorly understood. To elucidate the cellular program governing enucleation, we utilized a novel chemical screening approach whereby orthochromatic cells primed for enucleation were enriched ex vivo and subjected to a functional drug screen using a 324 compound library consisting of structurally diverse, medicinally active and cell permeable drugs. Using this approach, we have confirmed the role of HDACs, proteasomal regulators and MAPK in erythroid enucleation and introduce a new role for Cyclin-dependent kinases, in particular CDK9, in this process. Importantly, we demonstrate that when coupled with imaging analysis, this approach provides a powerful means to identify and characterize rate limiting steps involved in the erythroid enucleation process.     

A Hierarchical Approach to Cooperativity in Macromolecular and Self-Assembling Binding Systems

The study of complex macromolecular binding systems reveals that a high number of states and processes are involved in their mechanism of action, as has become more apparent with the sophistication of the experimental techniques used. The resulting information is often difficult to interpret because of the complexity of the scheme (large size and profuse interactions, including cooperative and self-assembling interactions) and the lack of transparency that this complexity introduces into the interpretation of the indexes traditionally used to describe the binding properties. In particular, cooperative behaviour can be attributed to very different causes, such as direct chemical modification of the binding sites, conformational changes in the whole structure of the macromolecule, aggregation processes between different subunits, etc. In this paper, we propose a novel approach for the analysis of the binding properties of complex macromolecular and self-assembling systems. To quantify the binding behaviour, we use the global association quotient defined as K _c = [occupied sites]/([free sites] L), L being the free ligand concentration. K _c can be easily related to other measures of cooperativity (such as the Hill number or the Scatchard plot) and to the free energies involved in the binding processes at each ligand concentration. In a previous work, it was shown that K_c could be decomposed as an average of equilibrium constants in two ways: intrinsic constants for Adair binding systems and elementary constants for the general case. In this study, we show that these two decompositions are particular cases of a more general expression, where the average is over partial association quotients, associated with subsystems from which the system is composed. We also show that if the system is split into different subsystems according to a binding hierarchy that starts from the lower, microscopic level and ends at the higher, aggregation level, the global association quotient can be decomposed following the hierarchical levels of macromolecular organisation. In this process, the partial association quotients of one level are expressed, in a recursive way, as a function of the partial quotients of the level that is immediately below, until the microscopic level is reached. As a result, the binding properties of very complex macromolecular systems can be analysed in detail, making the mechanistic explanation of their behaviour transparent. In addition, our approach provides a model-independent interpretation of the intrinsic equilibrium constants in terms of the elementary ones.