Small RNAs from plants, bacteria and fungi within the order Hypocreales are ubiquitous in human plasma

Background

The human microbiome plays a significant role in maintaining normal physiology. Changes in its composition have been associated with bowel disease, metabolic disorders and atherosclerosis. Sequences of microbial origin have been observed within small RNA sequencing data obtained from blood samples. The aim of this study was to characterise the microbiome from which these sequences are derived.

Abundant non-human small RNA sequences were identified in plasma and plasma exosomal samples. Assembly of these short sequences into longer contigs was the pivotal novel step in ascertaining their origin by BLAST searches. Most reads mapped to rRNA sequences. The taxonomic profiles of the microbes detected were very consistent between individuals but distinct from microbiomes reported at other sites. The majority of bacterial reads were from the phylum Proteobacteria, whilst for 5 of 6 individuals over 90% of the more abundant fungal reads were from the phylum Ascomycota; of these over 90% were from the order Hypocreales. Many contigs were from plants, presumably of dietary origin.  In addition, extremely abundant small RNAs derived from human Y RNAs were detected.

A characteristic profile of a subset of the human microbiome can be obtained by sequencing small RNAs present in the blood. The source and functions of these molecules remain to be determined, but the specific profiles are likely to reflect health status. The potential to provide biomarkers of diet and for the diagnosis and prognosis of human disease is immense.

Determinants of root-associated fungal communities within Asteraceae in a semi-arid grassland

Summary

1.While plant–fungal interactions are important determinants of plant community assembly and ecosystem functioning, the processes underlying fungal community composition are poorly understood.
2.Here, we studied for the first time the root-associated eumycotan communities in a set of co-occurring plant species of varying relatedness in a species-rich, semi-arid grassland in Germany. The study system provides an opportunity to evaluate the importance of host plants and gradients in soil type and landscape structure as drivers of fungal community structure on a relevant spatial scale. We used 454 pyrosequencing of the fungal internal transcribed spacer region to analyse root-associated eumycotan communities of 25 species within the Asteraceae, which were sampled at different locations within a soil type gradient. We partitioned the variance accounted for by three predictors (host plant phylogeny, spatial distribution and soil type) to quantify their relative roles in determining fungal community composition and used null model analyses to determine whether community composition was influenced by biotic interactions among the fungi.
3.We found a high fungal diversity (156 816 sequences clustered in 1100 operational taxonomic units (OTUs)). Most OTUs belonged to the phylum Ascomycota (35.8%); the most abundant phylotype best-matched Phialophora mustea. Basidiomycota were represented by 18.3%, with Sebacina as most abundant genus. The three predictors explained 30% of variation in the community structure of root-associated fungi, with host plant phylogeny being the most important variance component. Null model analysis suggested that many fungal taxa co-occurred less often than expected by chance, which demonstrates spatial segregation and indicates that negative interactions may prevail in the assembly of fungal communities.
4.Synthesis. The results show that the phylogenetic relationship of host plants is the most important predictor of root-associated fungal community assembly, indicating that fungal colonization of host plants might be facilitated by certain plant traits that may be shared among closely related plant species.

A markerless deletion method for genetic manipulation of Burkholderia cenocepacia and other multidrug-resistant gram-negative bacteria

Genetic manipulation of multidrug-resistant bacteria is often difficult and hinders progress in understanding their physiology and pathogenesis. This book chapter highlights advances in genetic manipulation of Burkholderia cenocepacia, which are also applicable to other members of the Burkholderia cepacia complex and multidrug-resistant gram-negative bacteria of other genera. The method detailed here is based on the I-SceI homing endonuclease system, which can be efficiently used for chromosomal integration, deletion, and genetic replacement. This system creates markerless mutations and insertions without leaving a genetic scar and thus can be reused successively to generate multiple modifications in the same strain.

Root Cause Analysis by a Combined Sparse Classification and Monte Carlo Approach

Classification methods with embedded feature selection capability are very appealing for the analysis of complex processes since they allow the analysis of root causes even when the number of input variables is high. In this work, we investigate the performance of three techniques for classification within a Monte Carlo strategy with the aim of root cause analysis. We consider the naive bayes classifier and the logistic regression model with two different implementations for controlling model complexity, namely, a LASSO-like implementation with a L1 norm regularization and a fully Bayesian implementation of the logistic model, the so called relevance vector machine. Several challenges can arise when estimating such models mainly linked to the characteristics of the data: a large number of input variables, high correlation among subsets of variables, the situation where the number of variables is higher than the number of available data points and the case of unbalanced datasets. Using an ecological and a semiconductor manufacturing dataset, we show advantages and drawbacks of each method, highlighting the superior performance in term of classification accuracy for the relevance vector machine with respect to the other classifiers. Moreover, we show how the combination of the proposed techniques and the Monte Carlo approach can be used to get more robust insights into the problem under analysis when faced with challenging modelling conditions.

Cough-generated aerosols of Pseudomonas aeruginosa and other Gram-negative bacteria from patients with cystic fibrosis

BACKGROUND: Pseudomonas aeruginosa is the most common bacterial pathogen in patients with cystic fibrosis (CF). Current infection control guidelines aim to prevent transmission via contact and respiratory droplet routes and do not consider the possibility of airborne transmission. It was hypothesised that subjects with CF produce viable respirable bacterial aerosols with coughing.

METHODS: A cross-sectional study was undertaken of 15 children and 13 adults with CF, 26 chronically infected with P aeruginosa. A cough aerosol sampling system enabled fractioning of respiratory particles of different sizes and culture of viable Gram-negative non-fermentative bacteria. Cough aerosols were collected during 5 min of voluntary coughing and during a sputum induction procedure when tolerated. Standardised quantitative culture and genotyping techniques were used.

RESULTS: P aeruginosa was isolated in cough aerosols of 25 subjects (89%), 22 of whom produced sputum samples. P aeruginosa from sputum and paired cough aerosols were indistinguishable by molecular typing. In four cases the same genotype was isolated from ambient room air. Approximately 70% of viable aerosols collected during voluntary coughing were of particles <or=3.3 microm aerodynamic diameter. P aeruginosa, Burkholderia cenocepacia, Stenotrophomonas maltophilia and Achromobacter xylosoxidans were cultivated from respiratory particles in this size range. Positive room air samples were associated with high total counts in cough aerosols (p = 0.003). The magnitude of cough aerosols was associated with higher forced expiratory volume in 1 s (r = 0.45, p = 0.02) and higher quantitative sputum culture results (r = 0.58, p = 0.008).

CONCLUSION: During coughing, patients with CF produce viable aerosols of P aeruginosa and other Gram-negative bacteria of respirable size range, suggesting the potential for airborne transmission.

Antibiotic Management of Lung Infections in Cystic Fibrosis. I. The Microbiome, Methicillin-Resistant Staphylococcus aureus, Gram-Negative Bacteria, and Multiple Infections

Despite significant advances in treatment strategies targeting the underlying defect in cystic fibrosis (CF), airway infection remains an important cause of lung disease. In this two-part series, we review recent evidence related to the complexity of CF airway infection, explore data suggesting the relevance of individual microbial species, and discuss current and future treatment options. In Part I, the evidence with respect to the spectrum of bacteria present in the CF airway, known as the lung microbiome is discussed. Subsequently, the current approach to treat methicillin-resistant Staphylococcus aureus, gram-negative bacteria, as well as multiple coinfections is reviewed. Newer molecular techniques have demonstrated that the airway microbiome consists of a large number of microbes, and the balance between microbes, rather than the mere presence of a single species, may be relevant for disease pathophysiology. A better understanding of this complex environment could help define optimal treatment regimens that target pathogens without affecting others. Although relevance of these organisms is unclear, the pathologic consequences of methicillin-resistant S. aureus infection in patients with CF have been recently determined. New strategies for eradication and treatment of both acute and chronic infections are discussed. Pseudomonas aeruginosa plays a prominent role in CF lung disease, butmany other nonfermenting gram-negative bacteria are also found in the CF airway. Many new inhaled antibiotics specifically targeting P. aeruginosa have become available with the hope that they will improve the quality of life for patients. Part I concludes with a discussion of how best to treat patients with multiple coinfections.