Development of the preterm gut microbiome in twins at risk of necrotising enterocolitis and sepsis

The preterm gut microbiome is a complex dynamic community influenced by genetic and environmental factors and is implicated in the pathogenesis of necrotising enterocolitis (NEC) and sepsis. We aimed to explore the longitudinal development of the gut microbiome in preterm twins to determine how shared environmental and genetic factors may influence temporal changes and compared this to the expressed breast milk (EBM) microbiome. Stool samples (n = 173) from 27 infants (12 twin pairs and 1 triplet set) and EBM (n = 18) from 4 mothers were collected longitudinally. All samples underwent PCR-DGGE (denaturing gradient gel electrophoresis) analysis and a selected subset underwent 454 pyrosequencing. Stool and EBM shared a core microbiome dominated by Enterobacteriaceae, Enterococcaceae, and Staphylococcaceae. The gut microbiome showed greater similarity between siblings compared to unrelated individuals. Pyrosequencing revealed a reduction in diversity and increasing dominance of Escherichia sp. preceding NEC that was not observed in the healthy twin. Antibiotic treatment had a substantial effect on the gut microbiome, reducing Escherichia sp. and increasing other Enterobacteriaceae. This study demonstrates related preterm twins share similar gut microbiome development, even within the complex environment of neonatal intensive care. This is likely a result of shared genetic and immunomodulatory factors as well as exposure to the same maternal microbiome during birth, skin contact and exposure to EBM. Environmental factors including antibiotic exposure and feeding are additional significant determinants of community structure, regardless of host genetics

Analysis of polyethylene wear in plain radiographs: The number of radiographs influences the results

Background and purpose Two-dimensional computerized radiographic techniques are frequently used to measure in vivo polyethylene (PE) wear after total hip arthroplasty (THA), and several variables in the clinical set-up may influence the amount of wear that is measured. We compared the repeatability and concurrent validity of linear PE wear on plain radiographs using the same software but a different number of radiographs

Is demography destiny? Application of machine learning techniques to accurately predict population health outcomes from a minimal demographic dataset

© 2015 Luo et al. For years, we have relied on population surveys to keep track of regional public health statistics, including the prevalence of non-communicable diseases. Because of the cost and limitations of such surveys, we often do not have the up-to-date data on health outcomes of a region. In this paper, we examined the feasibility of inferring regional health outcomes from socio-demographic data that are widely available and timely updated through national censuses and community surveys. Using data for 50 American states (excluding Washington DC) from 2007 to 2012, we constructed a machine-learning model to predict the prevalence of six non-communicable disease (NCD) outcomes (four NCDs and two major clinical risk factors), based on population socio-demographic characteristics from the American Community Survey. We found that regional prevalence estimates for non-communicable diseases can be reasonably predicted. The predictions were highly correlated with the observed data, in both the states included in the derivation model (median correlation 0.88) and those excluded from the development for use as a completely separated validation sample (median correlation 0.85), demonstrating that the model had sufficient external validity to make good predictions, based on demographics alone, for areas not included in the model development. This highlights both the utility of this sophisticated approach to model development, and the vital importance of simple socio-demographic characteristics as both indicators and determinants of chronic disease

Culture Enriched Molecular Profiling of the Cystic Fibrosis Airway Microbiome

The microbiome of the respiratory tract, including the nasopharyngeal and oropharyngeal microbiota, is a dynamic community of microorganisms that is highly diverse. The cystic fibrosis (CF) airway microbiome refers to the polymicrobial communities present in the lower airways of CF patients. It is comprised of chronic opportunistic pathogens (such as Pseudomonas aeruginosa) and a variety of organisms derived mostly from the normal microbiota of the upper respiratory tract. The complexity of these communities has been inferred primarily from culture independent molecular profiling. As with most microbial communities it is generally assumed that most of the organisms present are not readily cultured. Our culture collection generated using more extensive cultivation approaches, reveals a more complex microbial community than that obtained by conventional CF culture methods. To directly evaluate the cultivability of the airway microbiome, we examined six samples in depth using culture-enriched molecular profiling which combines culture-based methods with the molecular profiling methods of terminal restriction fragment length polymorphisms and 16S rRNA gene sequencing. We demonstrate that combining culture-dependent and culture-independent approaches enhances the sensitivity of either approach alone. Our techniques were able to cultivate 43 of the 48 families detected by deep sequencing; the five families recovered solely by culture-independent approaches were all present at very low abundance (<0.002% total reads). 46% of the molecular signatures detected by culture from the six patients were only identified in an anaerobic environment, suggesting that a large proportion of the cultured airway community is composed of obligate anaerobes. Most significantly, using 20 growth conditions per specimen, half of which included anaerobic cultivation and extended incubation times we demonstrate that the majority of bacteria present can be cultured

Impact of plants on the diversity and activity of methylotrophs in soil

Background Methanol is the second most abundant volatile organic compound in the atmosphere, with the majority produced as a metabolic by-product during plant growth. There is a large disparity between the estimated amount of methanol produced by plants and the amount which escapes to the atmosphere. This may be due to utilisation of methanol by plant-associated methanol-consuming bacteria (methylotrophs). The use of molecular probes has previously been effective in characterising the diversity of methylotrophs within the environment. Here, we developed and applied molecular probes in combination with stable isotope probing to identify the diversity, abundance and activity of methylotrophs in bulk and in plant-associated soils. Results Application of probes for methanol dehydrogenase genes (mxaF, xoxF, mdh2) in bulk and plant-associated soils revealed high levels of diversity of methylotrophic bacteria within the bulk soil, including Hyphomicrobium, Methylobacterium and members of the Comamonadaceae. The community of methylotrophic bacteria captured by this sequencing approach changed following plant growth. This shift in methylotrophic diversity was corroborated by identification of the active methylotrophs present in the soils by DNA stable isotope probing using 13C-labelled methanol. Sequencing of the 16S rRNA genes and construction of metagenomes from the 13C-labelled DNA revealed members of the Methylophilaceae as highly abundant and active in all soils examined. There was greater diversity of active members of the Methylophilaceae and Comamonadaceae and of the genus Methylobacterium in plant-associated soils compared to the bulk soil. Incubating growing pea plants in a 13CO2 atmosphere revealed that several genera of methylotrophs, as well as heterotrophic genera within the Actinomycetales, assimilated plant exudates in the pea rhizosphere. Conclusion In this study, we show that plant growth has a major impact on both the diversity and the activity of methanol-utilising methylotrophs in the soil environment, and thus, the study contributes significantly to efforts to balance the terrestrial methanol and carbon cycle

Mechanical model for a collagen fibril pair in extracellular matrix

In this paper, we model the mechanics of a collagen pair in the connective tissue extracellular matrix that exists in abundance throughout animals, including the human body. This connective tissue comprises repeated units of two main structures, namely collagens as well as axial, parallel and regular anionic glycosaminoglycan between collagens. The collagen fibril can be modeled by Hooke's law whereas anionic glycosaminoglycan behaves more like a rubber-band rod and as such can be better modeled by the worm-like chain model. While both computer simulations and continuum mechanics models have been investigated the behavior of this connective tissue typically, authors either assume a simple form of the molecular potential energy or entirely ignore the microscopic structure of the connective tissue. Here, we apply basic physical methodologies and simple applied mathematical modeling techniques to describe the collagen pair quantitatively. We find that the growth of fibrils is intimately related to the maximum length of the anionic glycosaminoglycan and the relative displacement of two adjacent fibrils, which in return is closely related to the effectiveness of anionic glycosaminoglycan in transmitting forces between fibrils. These reveal the importance of the anionic glycosaminoglycan in maintaining the structural shape of the connective tissue extracellular matrix and eventually the shape modulus of human tissues. We also find that some macroscopic properties, like the maximum molecular energy and the breaking fraction of the collagen, are also related to the microscopic characteristics of the anionic glycosaminoglycan