Keith Harris

Decreased gut microbiota diversity, delayed Bacteroidetes colonisation and reduced Th1 responses in infants delivered by Caesarean section

Objective The early intestinal microbiota exerts important stimuli for immune development, and a reduced microbial exposure as well as caesarean section (CS) has been associated with the development of allergic disease. Here we address how microbiota development in infants is affected by mode of delivery, and relate differences in colonisation patterns to the maturation of a balanced Th1/Th2 immune response. Design The postnatal intestinal colonisation pattern was investigated in 24 infants, born vaginally (15) or by CS (nine). The intestinal microbiota were characterised using pyrosequencing of 16S rRNA genes at 1 week and 1, 3, 6, 12 and 24 months after birth. Venous blood levels of Th1- and Th2-associated chemokines were measured at 6, 12 and 24 months. Results Infants born through CS had lower total microbiota diversity during the first 2 years of life. CS delivered infants also had a lower abundance and diversity of the Bacteroidetes phylum and were less often colonised with the Bacteroidetes phylum. Infants born through CS had significantly lower levels of the Th1-associated chemokines CXCL10 and CXCL11 in blood. Conclusions CS was associated with a lower total microbial diversity, delayed colonisation of the Bacteroidetes phylum and reduced Th1 responses during the first 2 years of life.

Dirichlet Multinomial Mixtures: Generative Models for Microbial Metagenomics

We introduce Dirichlet multinomial mixtures (DMM) for the probabilistic modelling of microbial metagenomics data. This data can be represented as a frequency matrix giving the number of times each taxa is observed in each sample. The samples have different size, and the matrix is sparse, as communities are diverse and skewed to rare taxa. Most methods used previously to classify or cluster samples have ignored these features. We describe each community by a vector of taxa probabilities. These vectors are generated from one of a finite number of Dirichlet mixture components each with different hyperparameters. Observed samples are generated through multinomial sampling. The mixture components cluster communities into distinct ‘metacommunities’, and, hence, determine envirotypes or enterotypes, groups of communities with a similar composition. The model can also deduce the impact of a treatment and be used for classification. We wrote software for the fitting of DMM models using the ‘evidence framework’ (http://code.google.com/p/microbedmm/). This includes the Laplace approximation of the model evidence. We applied the DMM model to human gut microbe genera frequencies from Obese and Lean twins. From the model evidence four clusters fit this data best. Two clusters were dominated by Bacteroides and were homogenous; two had a more variable community composition. We could not find a significant impact of body mass on community structure. However, Obese twins were more likely to derive from the high variance clusters. We propose that obesity is not associated with a distinct microbiota but increases the chance that an individual derives from a disturbed enterotype. This is an example of the ‘Anna Karenina principle (AKP)’ applied to microbial communities: disturbed states having many more configurations than undisturbed. We verify this by showing that in a study of inflammatory bowel disease (IBD) phenotypes, ileal Crohns disease (ICD) is associated with a more variable community.

Addressing the Challenge of Defining Valid Proteomic Biomarkers and Classifiers

BackgroundThe purpose of this manuscript is to provide, based on an extensive analysis of a proteomic data set, suggestions for proper statistical analysis for the discovery of sets of clinically relevant biomarkers. As tractable example we define the measurable proteomic differences between apparently healthy adult males and females. We choose urine as body-fluid of interest and CE-MS, a thoroughly validated platform technology, allowing for routine analysis of a large number of samples. The second urine of the morning was collected from apparently healthy male and female volunteers (aged 21-40) in the course of the routine medical check-up before recruitment at the Hannover Medical School.ResultsWe found that the Wilcoxon-test is best suited for the definition of potential biomarkers. Adjustment for multiple testing is necessary. Sample size estimation can be performed based on a small number of observations via resampling from pilot data. Machine learning algorithms appear ideally suited to generate classifiers. Assessment of any results in an independent test-set is essential.ConclusionsValid proteomic biomarkers for diagnosis and prognosis only can be defined by applying proper statistical data mining procedures. In particular, a justification of the sample size should be part of the study design.

Evolution of the plankton paleome in the Black Sea from the Deglacial to Anthropocene.

The complex interplay of climate shifts over Eurasia and global sea level changes modulates freshwater and saltwater inputs to the Black Sea. The dynamics of the hydrologic changes from the Late Glacial into the Holocene remain a matter of debate, and information on how these changes affected the ecology of the Black Sea is sparse. Here we used Roche 454 next-generation pyrosequencing of sedimentary 18S rRNA genes to reconstruct the plankton community structure in the Black Sea over the last ca. 11,400 y. We found that 150 of 2,710 species showed a statistically significant response to four environmental stages. Freshwater chlorophytes were the best indicator species for lacustrine conditions (>9.0 ka B.P.), although the copresence of previously unidentified marine taxa indicated that the Black Sea might have been influenced to some extent by the Marmara Sea since at least 9.6 ka calendar (cal) B.P. Dinoflagellates, cercozoa, eustigmatophytes, and haptophytes responded most dramatically to the gradual increase in salinity after the latest marine reconnection and during the warm and moist mid-Holocene climatic optimum. According to paired analysis of deuterium/hydrogen (D/H) isotope ratios in fossil alkenones, salinity increased rapidly with the onset of the dry Subboreal after ∼5.2 ka B.P., leading to an increase in marine fungi and the first occurrence of marine copepods. A gradual succession of dinoflagellates, diatoms, and chrysophytes occurred during the refreshening after ∼2.5 ka cal B.P. with the onset of the cool and wet Subatlantic climate and recent anthropogenic perturbations.

Linking Statistical and Ecological Theory: Hubbell's Unified Neutral Theory of Biodiversity as a Hierarchical Dirichlet Process

Neutral models which assume ecological equivalence between species provide null models for community assembly. In Hubbells unified neutral theory of biodiversity (UNTB), many local communities are connected to a single metacommunity through differing immigration rates. Our ability to fit the full multisite UNTB has hitherto been limited by the lack of a computationally tractable and accurate algorithm. We show that a large class of neutral models with this mainland-island structure but differing local community dynamics converge in the large population limit to the hierarchical Dirichlet process. Using this approximation we developed an efficient Bayesian fitting strategy for the multisite UNTB. We can also use this approach to distinguish between neutral local community assembly given a nonneutral metacommunity distribution and the full UNTB where the metacommunity too assembles neutrally. We applied this fitting strategy to both tropical trees and a data set comprising 570

A Multivariate Control Chart for Autocorrelated Tool Wear Processes

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Definition of Valid Proteomic Biomarkers: A Bayesian Solution

851 sequences from 278 human gut microbiomes. The tropical tree data set was consistent with the UNTB but for the human gut neutrality was rejected at the whole community level. However, when we applied the algorithm to gut microbial species within the same taxon at different levels of taxonomic resolution, we found that species abundances within some genera were almost consistent with local community assembly. This was not true at higher taxonomic ranks. This suggests that the gut microbiota is more strongly niche constrained than macroscopic organisms, with different groups adopting different functional roles, but within those groups diversity may at least partially be maintained by neutrality. We also observed a negative correlation between body mass index and immigration rates within the family Ruminococcaceae. This provides a novel interpretation of the impact of obesity on the human microbiome as a relative increase in the importance of local growth versus external immigration within this key group of carbohydrate degrading organisms.

Flexible continuous-time modelling for heterogeneous animal movement

Full automation of metal cutting processes has been a long held goal of the manufacturing industry. One key obstacle to achieving this ambition has been the inability to monitor completely the condition of the cutting tool in real time, as premature tool breakage and heavy tool wear can result in substantial costs through damage to the machinery and increasing the risk of non-conforming items that have to be scrapped or reworked. Instead, the condition of the tool has to be indirectly monitored using modern sensor technology that measures the acoustic emission, sound, spindle power and vibration of the tool during a cut. An on-line monitoring procedure for such data is proposed. Firstly, the standard deviation is extracted from each sensor signal to summarise the state of the tool after each cut. Secondly, a multivariate autoregressive state space model is specified for estimating the joint effects and cross-correlation of the sensor variables in Phase I. Then we apply a distribution-free monitoring scheme to the model residuals in Phase II, based on binomial type statistics. The proposed methodology is illustrated using a case study of titanium alloy milling (a machining process used in the manufacture of aircraft landing gears) from the Advanced Manufacturing Research Centre in Sheffield, UK, and is demonstrated to outperform alternative residual control charts in this application.

Inferring Meta-covariates in Classification

Clinical proteomics is suffering from high hopes generated by reports on apparent biomarkers, most of which could not be later substantiated via validation. This has brought into focus the need for improved methods of finding a panel of clearly defined biomarkers. To examine this problem, urinary proteome data was collected from healthy adult males and females, and analysed to find biomarkers that differentiated between genders. We believe that models that incorporate sparsity in terms of variables are desirable for biomarker selection, as proteomics data typically contains a huge number of variables (peptides) and few samples making the selection process potentially unstable. This suggests the application of a two-level hierarchical Bayesian probit regression model for variable selection which assumes a prior that favours sparseness. The classification performance of this method is shown to improve that of the Probabilistic K-Nearest Neighbour model.