Early childhood gut microbiomes show strong geographic differences among subjects at high risk for type 1 diabetes.

OBJECTIVE: Gut microbiome dysbiosis is associated with numerous diseases, including type 1 diabetes. This pilot study determines how geographical location affects the microbiome of infants at high risk for type 1 diabetes in a population of homogenous HLA class II genotypes. RESEARCH DESIGN AND METHODS: High-throughput 16S rRNA sequencing was performed on stool samples collected from 90 high-risk, nonautoimmune infants participating in The Environmental Determinants of Diabetes in the Young (TEDDY) study in the U.S., Germany, Sweden, and Finland. RESULTS: Study site-specific patterns of gut colonization share characteristics across continents. Finland and Colorado have a significantly lower bacterial diversity, while Sweden and Washington state are dominated by Bifidobacterium in early life. Bacterial community diversity over time is significantly different by geographical location. CONCLUSIONS: The microbiome of high-risk infants is associated with geographical location. Future studies aiming to identify the microbiome disease phenotype need to carefully consider the geographical origin of subjects.

Pyrokinin ß-neuropeptide affects necrophoretic behavior in fire ants (S. invicta), and expression of ß-NP in a mycoinsecticide increases its virulence.

Fire ants are one of the world's most damaging invasive pests, with few means for their effective control. Although ecologically friendly alternatives to chemical pesticides such as the insecticidal fungus Beauveria bassiana have been suggested for the control of fire ant populations, their use has been limited due to the low virulence of the fungus and the length of time it takes to kill its target. We present a means of increasing the virulence of the fungal agent by expressing a fire ant neuropeptide. Expression of the fire ant (Solenopsis invicta) pyrokinin ß-neuropeptide (ß-NP) by B. bassiana increased fungal virulence six-fold towards fire ants, decreased the LT(50), but did not affect virulence towards the lepidopteran, Galleria mellonella. Intriguingly, ants killed by the ß-NP expressing fungus were disrupted in the removal of dead colony members, i.e. necrophoretic behavior. Furthermore, synthetic C-terminal amidated ß-NP but not the non-amidated peptide had a dramatic effect on necrophoretic behavior. These data link chemical sensing of a specific peptide to a complex social behavior. Our results also confirm a new approach to insect control in which expression of host molecules in an insect pathogen can by exploited for target specific augmentation of virulence. The minimization of the development of potential insect resistance by our approach is discussed.

A regionalised strategy for improving motor vehicle-related highway driver deaths using a weighted averages method.

OBJECTIVE: The state of Florida has some of the most dangerous highways in the USA. In 2006, Florida averaged 1.65 fatalities per 100 million vehicle miles travelled (VMT) compared with the national average of 1.42. A study was undertaken to find a method of identifying counties that contributed to the most driver fatalities after a motor vehicle collision (MVC). By regionalising interventions unique to this subset of counties, the use of resources would have the greatest potential of improving statewide driver death. METHODS: The Florida Highway Safety Motor Vehicle database 2000-2006 was used to calculate driver VMT-weighted deaths by county. A total of 3,468,326 motor vehicle crashes were evaluated. Counties that had driver death rates higher than the state average were sorted by a weighted averages method. Multivariate regression was used to calculate the likelihood of death for various risk factors. RESULTS: VMT-weighted death rates identified 12 out of 67 counties that contributed up to 50% of overall driver fatalities. These counties were primarily clustered in central and south Florida. The strongest independent risk factors for driver death attributable to MVC in these high-risk counties were alcohol/drug use, rural roads, speed limit ≥45 mph, adverse weather conditions, divided highways, vehicle type, vehicle defects and roadway location. CONCLUSIONS: Using the weighted averages method, a small subset of counties contributing to the majority of statewide driver fatalities was identified. Regionalised interventions on specific risk factors in these counties may have the greatest impact on reducing driver-related MVC fatalities.

Objective Bayes model selection in probit models.

We describe a new variable selection procedure for categorical responses where the candidate models are all probit regression models. The procedure uses objective intrinsic priors for the model parameters, which do not depend on tuning parameters, and ranks the models for the different subsets of covariates according to their model posterior probabilities. When the number of covariates is moderate or large, the number of potential models can be very large, and for those cases, we derive a new stochastic search algorithm that explores the potential sets of models driven by their model posterior probabilities. The algorithm allows the user to control the dimension of the candidate models and thus can handle situations when the number of covariates exceed the number of observations. We assess, through simulations, the performance of the procedure and apply the variable selector to a gene expression data set, where the response is whether a patient exhibits pneumonia. Software needed to run the procedures is available in the R package varselectIP.

Gut microbiome metagenomics analysis suggests a functional model for the development of autoimmunity for type 1 diabetes.

Recent studies have suggested a bacterial role in the development of autoimmune disorders including type 1 diabetes (T1D). Over 30 billion nucleotide bases of Illumina shotgun metagenomic data were analyzed from stool samples collected from four pairs of matched T1D case-control subjects collected at the time of the development of T1D associated autoimmunity (i.e., autoantibodies). From these, approximately one million open reading frames were predicted and compared to the SEED protein database. Of the 3,849 functions identified in these samples, 144 and 797 were statistically more prevalent in cases and controls, respectively. Genes involved in carbohydrate metabolism, adhesions, motility, phages, prophages, sulfur metabolism, and stress responses were more abundant in cases while genes with roles in DNA and protein metabolism, aerobic respiration, and amino acid synthesis were more common in controls. These data suggest that increased adhesion and flagella synthesis in autoimmune subjects may be involved in triggering a T1D associated autoimmune response. Extensive differences in metabolic potential indicate that autoimmune subjects have a functionally aberrant microbiome. Mining 16S rRNA data from these datasets showed a higher proportion of butyrate-producing and mucin-degrading bacteria in controls compared to cases, while those bacteria that produce short chain fatty acids other than butyrate were higher in cases. Thus, a key rate-limiting step in butyrate synthesis is more abundant in controls. These data suggest that a consortium of lactate- and butyrate-producing bacteria in a healthy gut induce a sufficient amount of mucin synthesis to maintain gut integrity. In contrast, non-butyrate-producing lactate-utilizing bacteria prevent optimal mucin synthesis, as identified in autoimmune subjects.

Generalized shrinkage F-like statistics for testing an interaction term in gene expression analysis in the presence of heteroscedasticity.

BACKGROUND: Many analyses of gene expression data involve hypothesis tests of an interaction term between two fixed effects, typically tested using a residual variance. In expression studies, the issue of variance heteroscedasticity has received much attention, and previous work has focused on either between-gene or within-gene heteroscedasticity. However, in a single experiment, heteroscedasticity may exist both within and between genes. Here we develop flexible shrinkage error estimators considering both between-gene and within-gene heteroscedasticity and use them to construct F-like test statistics for testing interactions, with cutoff values obtained by permutation. These permutation tests are complicated, and several permutation tests are investigated here. RESULTS: Our proposed test statistics are compared with other existing shrinkage-type test statistics through extensive simulation studies and a real data example. The results show that the choice of permutation procedures has dramatically more influence on detection power than the choice of F or F-like test statistics. When both types of gene heteroscedasticity exist, our proposed test statistics can control preselected type-I errors and are more powerful. Raw data permutation is not valid in this setting. Whether unrestricted or restricted residual permutation should be used depends on the specific type of test statistic. CONCLUSIONS: The F-like test statistic that uses the proposed flexible shrinkage error estimator considering both types of gene heteroscedasticity and unrestricted residual permutation can provide a statistically valid and powerful test. Therefore, we recommended that it should always applied in the analysis of real gene expression data analysis to test an interaction term.

Interval estimation in two-stage, drop-the-losers clinical trials with flexible treatment selection.

In a two-stage, drop-the-losers clinical trial, researchers choose the 'best' among a number of treatments at an interim analysis after the first stage. The selected treatment continues to the second stage for confirmation of efficacy, and the remaining treatments (the 'losers') are dropped from the study. Wu et al. (Biometrika 2010; 97:405-418) showed how to construct confidence limits for the mean difference between the selected treatment and the control when the treatment is chosen after the first stage based on the highest efficacy in the primary clinical endpoint. In this article, we show how to construct a lower confidence limit for the mean difference when the treatment is chosen based on first-stage safety data, early endpoint efficacy data, a combination of safety and efficacy data or any other prespecified selection rule. The result extends the applicability of drop-the-losers designs, for in practice, the 'best' treatment often is not chosen for efficacy alone.

Fecal microbiota in premature infants prior to necrotizing enterocolitis.

Intestinal luminal microbiota likely contribute to the etiology of necrotizing enterocolitis (NEC), a common disease in preterm infants. Microbiota development, a cascade of initial colonization events leading to the establishment of a diverse commensal microbiota, can now be studied in preterm infants using powerful molecular tools. Starting with the first stool and continuing until discharge, weekly stool specimens were collected prospectively from infants with gestational ages ≤32 completed weeks or birth weights≤1250 g. High throughput 16S rRNA sequencing was used to compare the diversity of microbiota and the prevalence of specific bacterial signatures in nine NEC infants and in nine matched controls. After removal of short and low quality reads we retained a total of 110,021 sequences. Microbiota composition differed in the matched samples collected 1 week but not <72 hours prior to NEC diagnosis. We detected a bloom (34% increase) of Proteobacteria and a decrease (32%) in Firmicutes in NEC cases between the 1 week and <72 hour samples. No significant change was identified in the controls. At both time points, molecular signatures were identified that were increased in NEC cases. One of the bacterial signatures detected more frequently in NEC cases (p<0.01) matched closest to γ-Proteobacteria. Although this sequence grouped to the well-studied Enterobacteriaceae family, it did not match any sequence in Genbank by more than 97%. Our observations suggest that abnormal patterns of microbiota and potentially a novel pathogen contribute to the etiology of NEC.

The potential influence of common viral infections diagnosed during hospitalization among critically ill patients in the United States.

Viruses are the most common source of infection among immunocompetent individuals, yet they are not considered a clinically meaningful risk factor among the critically ill. This work examines the association of viral infections diagnosed during the hospital stay or not documented as present on admission to the outcomes of ICU patients with no evidence of immunosuppression on admission. This is a population-based retrospective cohort study of University HealthSystem Consortium (UHC) academic centers in the U.S. from the years 2006 to 2009. The UHC is an alliance of over 90% of the non-profit academic medical centers in the U.S. A total of 209,695 critically ill patients were used in this analysis. Eight hospital complications were examined. Patients were grouped into four cohorts: absence of infection, bacterial infection only, viral infection only, and bacterial and viral infection during same hospital admission. Viral infections diagnosed during hospitalization significantly increased the risk of all complications. There was also a seasonal pattern for viral infections. Specific viruses associated with poor outcomes included influenza, RSV, CMV, and HSV. Patients who had both viral and bacterial infections during the same hospitalization had the greatest risk of mortality RR 6.58, 95% CI (5.47, 7.91); multi-organ failure RR 8.25, 95% CI (7.50, 9.07); and septic shock RR 271.2, 95% CI (188.0, 391.3). Viral infections may play a significant yet unrecognized role in the outcomes of ICU patients. They may serve as biological markers or play an active role in the development of certain adverse complications by interacting with coincident bacterial infection.

Toward defining the autoimmune microbiome for type 1 diabetes.

Several studies have shown that gut bacteria have a role in diabetes in murine models. Specific bacteria have been correlated with the onset of diabetes in a rat model. However, it is unknown whether human intestinal microbes have a role in the development of autoimmunity that often leads to type 1 diabetes (T1D), an autoimmune disorder in which insulin-secreting pancreatic islet cells are destroyed. High-throughput, culture-independent approaches identified bacteria that correlate with the development of T1D-associated autoimmunity in young children who are at high genetic risk for this disorder. The level of bacterial diversity diminishes overtime in these autoimmune subjects relative to that of age-matched, genotype-matched, nonautoimmune individuals. A single species, Bacteroides ovatus, comprised nearly 24% of the total increase in the phylum Bacteroidetes in cases compared with controls. Conversely, another species in controls, represented by the human firmicute strain CO19, represented nearly 20% of the increase in Firmicutes compared with cases overtime. Three lines of evidence are presented that support the notion that, as healthy infants approach the toddler stage, their microbiomes become healthier and more stable, whereas, children who are destined for autoimmunity develop a microbiome that is less diverse and stable. Hence, the autoimmune microbiome for T1D may be distinctly different from that found in healthy children. These data also suggest bacterial markers for the early diagnosis of T1D. In addition, bacteria that negatively correlated with the autoimmune state may prove to be useful in the prevention of autoimmunity development in high-risk children.

Epistasis: obstacle or advantage for mapping complex traits?

Identification of genetic loci in complex traits has focused largely on one-dimensional genome scans to search for associations between single markers and the phenotype. There is mounting evidence that locus interactions, or epistasis, are a crucial component of the genetic architecture of biologically relevant traits. However, epistasis is often viewed as a nuisance factor that reduces power for locus detection. Counter to expectations, recent work shows that fitting full models, instead of testing marker main effect and interaction components separately, in exhaustive multi-locus genome scans can have higher power to detect loci when epistasis is present than single-locus scans, and improvement that comes despite a much larger multiple testing alpha-adjustment in such searches. We demonstrate, both theoretically and via simulation, that the expected power to detect loci when fitting full models is often larger when these loci act epistatically than when they act additively. Additionally, we show that the power for single locus detection may be improved in cases of epistasis compared to the additive model. Our exploration of a two step model selection procedure shows that identifying the true model is difficult. However, this difficulty is certainly not exacerbated by the presence of epistasis, on the contrary, in some cases the presence of epistasis can aid in model selection. The impact of allele frequencies on both power and model selection is dramatic.

Association mapping of quantitative disease resistance in a natural population of loblolly pine (Pinus taeda L.).

Genetic resistance to disease incited by necrotrophic pathogens is not well understood in plants. Whereas resistance is often quantitative, there is limited information on the genes that underpin quantitative variation in disease resistance. We used a population genomic approach to identify genes in loblolly pine (Pinus taeda) that are associated with resistance to pitch canker, a disease incited by the necrotrophic pathogen Fusarium circinatum. A set of 498 largely unrelated, clonally propagated genotypes were inoculated with F. circinatum microconidia and lesion length, a measure of disease resistance, data were collected 4, 8, and 12 weeks after inoculation. Best linear unbiased prediction was used to adjust for imbalance in number of observations and to identify highly susceptible and highly resistant genotypes ("tails"). The tails were reinoculated to validate the results of the full population screen. Significant associations were detected in 10 single nucleotide polymorphisms (SNPs) (out of 3938 tested). As hypothesized for genes involved in quantitative resistance, the 10 SNPs had small effects and proposed roles in basal resistance, direct defense, and signal transduction. We also discovered associated genes with unknown function, which would have remained undetected in a candidate gene approach constrained by annotation for disease resistance or stress response.

PANGEA: pipeline for analysis of next generation amplicons.

High-throughput DNA sequencing can identify organisms and describe population structures in many environmental and clinical samples. Current technologies generate millions of reads in a single run, requiring extensive computational strategies to organize, analyze and interpret those sequences. A series of bioinformatics tools for high-throughput sequencing analysis, including pre-processing, clustering, database matching and classification, have been compiled into a pipeline called PANGEA. The PANGEA pipeline was written in Perl and can be run on Mac OSX, Windows or Linux. With PANGEA, sequences obtained directly from the sequencer can be processed quickly to provide the files needed for sequence identification by BLAST and for comparison of microbial communities. Two different sets of bacterial 16S rRNA sequences were used to show the efficiency of this workflow. The first set of 16S rRNA sequences is derived from various soils from Hawaii Volcanoes National Park. The second set is derived from stool samples collected from diabetes-resistant and diabetes-prone rats. The workflow described here allows the investigator to quickly assess libraries of sequences on personal computers with customized databases. PANGEA is provided for users as individual scripts for each step in the process or as a single script where all processes, except the chi(2) step, are joined into one program called the 'backbone'.

Influence of fecal sample storage on bacterial community diversity.

Previous studies have identified a correlation, either positive or negative, between specific stool bacteria strains and certain autoimmune diseases. These conflicting data may relate to sample collection. The aim of this work was to evaluate the influence of the collection parameters of time and temperature on bacterial community composition. Samples were taken from healthy children and immediately divided in 5 sub-samples. One sample was frozen immediately at -80 ° C, while the other aliquots were frozen 12, 24, 48, and 72h later DNA extracted from each sample was used to amplify the 16S rRNA with barcoded primers. The amplified products were pooled and partial 16S rRNA sequences were obtained by pyrosequencing. Person-to-person variability in community diversity was high. A list of those taxa that comprise at least 1% of the community was made for each individual. None of these were present in high numbers in all individuals. The Bacteroides were present in the highest abundance in three of four subjects. A total of 23,701 16S rRNA sequences were obtained with an average of 1,185 reads per sample with an average length of 200 bases. Although pyrosequencing of amplified 16S rRNA identified changes in community composition over time (~10%), little diversity change was observed at 12 hours (3.06%) with gradual changes occurring after 24 (8.61%), 48 (9.72%), and 72 h (10.14%), post collection.

Culture-independent identification of gut bacteria correlated with the onset of diabetes in a rat model.

Bacteria associated with the onset of type 1 diabetes in a rat model system were identified. In two experiments, stool samples were collected at three time points after birth from bio-breeding diabetes-prone (BB-DP) and bio-breeding diabetes-resistant (BB-DR) rats. DNA was isolated from these samples and the 16S rRNA gene was amplified using universal primer sets. In the first experiment, bands specific to BB-DP and BB-DR genotypes were identified by automated ribosomal intergenic spacer analysis at the time of diabetes onset in BB-DP. Lactobacillus and Bacteroides strains were identified in the BB-DR- and BB-DP-specific bands, respectively. Sanger sequencing showed that the BB-DP and BB-DR bacterial communities differed significantly but too few reads were available to identify significant differences at the genus or species levels. A second experiment confirmed these results using higher throughput pyrosequencing and quantitative PCR of 16S rRNA with more rats per genotype. An average of 4541 and 3381 16S rRNA bacterial reads were obtained from each of the 10 BB-DR and 10 BB-DP samples collected at time of diabetes onset. Nine genera were more abundant in BB-DP whereas another nine genera were more abundant in BB-DR. Thirteen and eleven species were more abundant in BB-DP and BB-DR, respectively. An average of 23% and 10% of all reads could be classified at the genus and species levels, respectively. Quantitative PCR verified the higher abundance of Lactobacillus and Bifidobacterium in the BB-DR samples. Whether these changes are caused by diabetes or are involved in the development of the disease is unknown.

Score statistics for mapping quantitative trait loci.

We propose a method to detect the existence of quantitative trait loci (QTL) in a backcross population using a score test. Since the score test only uses the MLEs of parameters under the null hypothesis, it is computationally simpler than the likelihood ratio test (LRT). Moreover, because the location parameter of the QTL is unidentifiable under the null hypothesis, the distribution of the maximum of the LRT statistics, typically the statistic of choice for testing H0: no QTL, does not have the standard chi-square distribution asymptotically under the null hypothesis. From the simple structure of the score test statistics, the asymptotic null distribution can be derived for the maximum of the square of score test statistics. Numerical methods are proposed to compute the asymptotic null distribution and the critical thresholds can be obtained accordingly. We show that the maximum of the LR test statistics and the maximum of the square of score statistics are asymptotically equivalent. Therefore, the critical threshold for the score test can be used for the LR test also. A simple backcross design is used to demonstrate the application of the score test to QTL mapping.

Nonparametric functional mapping of quantitative trait loci.

Functional mapping is a useful tool for mapping quantitative trait loci (QTL) that control dynamic traits. It incorporates mathematical aspects of biological processes into the mixture model-based likelihood setting for QTL mapping, thus increasing the power of QTL detection and the precision of parameter estimation. However, in many situations there is no obvious functional form and, in such cases, this strategy will not be optimal. Here we propose to use nonparametric function estimation, typically implemented with B-splines, to estimate the underlying functional form of phenotypic trajectories, and then construct a nonparametric test to find evidence of existing QTL. Using the representation of a nonparametric regression as a mixed model, the final test statistic is a likelihood ratio test. We consider two types of genetic maps: dense maps and general maps, and the power of nonparametric functional mapping is investigated through simulation studies and demonstrated by examples.

Testing for the existence of clusters.

Detecting and determining clusters present in a certain sample has been an important concern, among researchers from different fields, for a long time. In particular, assessing whether the clusters are statistically significant, is a question that has been asked by a number of experimenters. Recently, this question arose again in a study in maize genetics, where determining the significance of clusters is crucial as a primary step in the identification of a genome-wide collection of mutants that may affect the kernel composition.Although several efforts have been made in this direction, not much has been done with the aim of developing an actual hypothesis test in order to assess the significance of clusters. In this paper, we propose a new methodology that allows the examination of the hypothesis test H(0) : κ=1 vs. H(1) : κ=k, where κ denotes the number of clusters present in a certain population. Our procedure, based on Bayesian tools, permits us to obtain closed form expressions for the posterior probabilities corresponding to the null hypothesis. From here, we calibrate our results by estimating the frequentist null distribution of the posterior probabilities in order to obtain the p-values associated with the observed posterior probabilities. In most cases, actual evaluation of the posterior probabilities is computationally intensive and several algorithms have been discussed in the literature. Here, we propose a simple estimation procedure, based on MCMC techniques, that permits an efficient and easily implementable evaluation of the test. Finally, we present simulation studies that support our conclusions, and we apply our method to the analysis of NIR spectroscopy data coming from the genetic study that motivated this work.

A preliminary gene expression profile of acute graft-versus-host disease.

Allogeneic hematopoietic stem cell transplantation (HSCT) is the treatment of choice for high-risk hematological malignancies, yet a major complication associated with this therapy is acute graft-versus-host disease (GVHD). Despite a well-defined pathophysiological mechanism, there are no definitive markers for predicting acute GVHD development or progression to advanced stages. In the current study, we enrolled four acute GVHD and four acute GVHD-free recipients of allogeneic HSCT and collected peripheral blood just prior to onset of clinical acute GVHD for analysis on Affymetrix GeneChip Human Genome U133 Plus 2.0 microarrays. We noted significant differences in expression of 1,658 genes between control and acute GVHD patients, based on an analysis of covariance (ANCOVA) by type of transplant, a pooled error estimate, and a false discovery rate (FDR) of 10%. In conclusion, we offer the first report of a preliminary molecular signature of acute GVHD in allogeneic HSCT patients.

Comparative analysis of the transcriptomes of Populus trichocarpa and Arabidopsis thaliana suggests extensive evolution of gene expression regulation in angiosperms.

Sequencing of the Populus trichocarpa genome creates an opportunity to describe the transcriptome of a woody perennial species and establish an atlas of gene expression. A comparison with the transcriptomes of other species can also define genes that are conserved or diverging in plant species. Here, the transcriptome in vegetative organs of the P. trichocarpa reference genotype Nisqually-1 was characterized. A comparison with Arabidopsis thaliana orthologs was used to distinguish gene functional categories that may be evolving differently in a woody perennial and an annual herbaceous species. A core set of genes expressed in common among vegetative organs was detected, as well as organ-specific genes. Statistical tests identified chromatin domains, where adjacent genes were expressed more frequently than expected by chance. Extensive divergence was detected in the expression patterns of A. thaliana and P. trichocarpa orthologs, but transcription of a small number of genes appeared to have remained conserved in the two species. Despite separation of lineages for over 100 million yr, these results suggest that selection has limited transcriptional divergence of genes associated with some essential functions in A. thaliana and P. trichocarpa. However, extensive remodeling of transcriptional networks indicates that expression regulation may be a key determinant of plant diversity.