Early reports indicate that long non-coding RNAs (lncRNAs) are novel regulators of biological responses. However, their role in the human innate immune response, which provides the initial defence against infection, is largely unexplored. To address this issue, here we characterize the long non-coding RNA transcriptome in primary human monocytes using RNA sequencing. We identify 76 enhancer RNAs (eRNAs), 40 canonical lncRNAs, 65 antisense lncRNAs and 35 regions of bidirectional transcription (RBT) that are differentially expressed in response to bacterial lipopolysaccharide (LPS). Crucially, we demonstrate that knockdown of nuclear-localized, NF-κB-regulated, eRNAs (IL1β-eRNA) and RBT (IL1β-RBT46) surrounding the IL1β locus, attenuates LPS-induced messenger RNA transcription and release of the proinflammatory mediators, IL1β and CXCL8. We predict that lncRNAs can be important regulators of the human innate immune response.

Several conditions associated with reduced gastric acid secretion confer an altered risk of developing a gastric malignancy. Helicobacter pylori-induced atrophic gastritis predisposes to gastric adenocarcinoma, autoimmune atrophic gastritis is a precursor of type I gastric neuroendocrine tumours, whereas proton pump inhibitor (PPI) use does not affect stomach cancer risk. We hypothesised that each of these conditions was associated with specific alterations in the gastric microbiota and that this influenced subsequent tumour risk. 95 patients (in groups representing normal stomach, PPI treated, H. pylori gastritis, H. pylori-induced atrophic gastritis and autoimmune atrophic gastritis) were selected from a cohort of 1400. RNA extracted from gastric corpus biopsies was analysed using 16S rRNA sequencing (MiSeq). Samples from normal stomachs and patients treated with PPIs demonstrated similarly high microbial diversity. Patients with autoimmune atrophic gastritis also exhibited relatively high microbial diversity, but with samples dominated by Streptococcus. H. pylori colonisation was associated with decreased microbial diversity and reduced complexity of co-occurrence networks. H. pylori-induced atrophic gastritis resulted in lower bacterial abundances and diversity, whereas autoimmune atrophic gastritis resulted in greater bacterial abundance and equally high diversity compared to normal stomachs. Pathway analysis suggested that glucose-6-phospahte1-dehydrogenase and D-lactate dehydrogenase were over represented in H. pylori-induced atrophic gastritis versus autoimmune atrophic gastritis, and that both these groups showed increases in fumarate reductase. Autoimmune and H. pylori-induced atrophic gastritis were associated with different gastric microbial profiles. PPI treated patients showed relatively few alterations in the gastric microbiota compared to healthy subjects.

The gut microbiota profoundly affects the biology of its host. The composition of the microbiota is dynamic and is affected by both host genetic and many environmental effects. The gut microbiota of laboratory mice has been studied extensively, which has uncovered many of the effects that the microbiota can have. This work has also shown that the environments of different research institutions can affect the mouse microbiota. There has been relatively limited study of the microbiota of wild mice, but this has shown that it typically differs from that of laboratory mice (and that maintaining wild caught mice in the laboratory can quite quickly alter the microbiota). There is also inter-individual variation in the microbiota of wild mice, with this principally explained by geographical location. In this study we have characterised the gut (both the caecum and rectum) microbiota of wild caught Mus musculus domesticus at three UK sites and have investigated how the microbiota varies depending on host location and host characteristics. We find that the microbiota of these mice are generally consistent with those described from other wild mice. The rectal and caecal microbiotas of individual mice are generally more similar to each other, than they are to the microbiota of other individuals. We found significant differences in the diversity of the microbiotas among mice from different sample sites. There were significant correlations of microbiota diversity and body weight, a measure of age, body-mass index, serum concentration of leptin, and virus, nematode and mite infection.

BackgroundSeveral tools have been developed to perform global gene expression profile data analysis, to search for specific chromosomal regions whose features meet defined criteria as well as to study neighbouring gene expression. However, most of these tools are tailored for a specific use in a particular context (e.g. they are species-specific, or limited to a particular data format) and they typically accept only gene lists as input.ResultsTRAM (Transcriptome Mapper) is a new general tool that allows the simple generation and analysis of quantitative transcriptome maps, starting from any source listing gene expression values for a given gene set (e.g. expression microarrays), implemented as a relational database. It includes a parser able to assign univocal and updated gene symbols to gene identifiers from different data sources. Moreover, TRAM is able to perform intra-sample and inter-sample data normalization, including an original variant of quantile normalization (scaled quantile), useful to normalize data from platforms with highly different numbers of investigated genes. When in 'Map' mode, the software generates a quantitative representation of the transcriptome of a sample (or of a pool of samples) and identifies if segments of defined lengths are over/under-expressed compared to the desired threshold. When in 'Cluster' mode, the software searches for a set of over/under-expressed consecutive genes. Statistical significance for all results is calculated with respect to genes localized on the same chromosome or to all genome genes. Transcriptome maps, showing differential expression between two sample groups, relative to two different biological conditions, may be easily generated. We present the results of a biological model test, based on a meta-analysis comparison between a sample pool of human CD34+ hematopoietic progenitor cells and a sample pool of megakaryocytic cells. Biologically relevant chromosomal segments and gene clusters with differential expression during the differentiation toward megakaryocyte were identified.ConclusionsTRAM is designed to create, and statistically analyze, quantitative transcriptome maps, based on gene expression data from multiple sources. The release includes FileMaker Pro database management runtime application and it is freely available at http://apollo11.isto.unibo.it/software/, along with preconfigured implementations for mapping of human, mouse and zebrafish transcriptomes.

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Marine sponges are hosts to large, diverse communities of microorganisms. These microbiomes are distinct among sponge species and from seawater bacterial communities, indicating a key role of host identity in shaping its resident microbial community. However, the factors governing intraspecific microbiome variability are underexplored and may shed light on the evolutionary and ecological relationships between host and microbiome.Here, we examined the influence of genetic variation and geographic location on the composition of the Ircinia campana microbiome.We developed new microsatellite markers to genotype I. campana from two locations in the Florida Keys, USA, and characterized their microbiomes using V4 16S rRNA amplicon sequencing.We show that microbial community composition and diversity is influenced by host genotype, with more genetically similar sponges hosting more similar microbial communities. We also found that although I. campana was not genetically differentiated between sites, microbiome composition differed by location.Our results demonstrate that both host genetics and geography influence the composition of the sponge microbiome. Host genotypic influence on microbiome composition may be due to stable vertical transmission of the microbial community from parent to offspring, making microbiomes more similar by descent. Alternatively, sponge genotypic variation may reflect variation in functional traits that influence the acquisition of environmental microbes. This study reveals drivers of microbiome variation within and among locations, and shows the importance of intraspecific variability in mediating eco‐evolutionary dynamics of host‐associated microbiomes.

Local adaptation of a species can affect community composition, yet the importance of local adaptation compared with species presence per se is unknown. Here we determine how a compost bacterial community exposed to elevated temperature changes over 2 months as a result of the presence of a focal bacterium, Pseudomonas fluorescens SBW25, that had been pre-adapted or not to the compost for 48 days. The effect of local adaptation on community composition is as great as the effect of species presence per se, with these results robust to the presence of an additional strong selection pressure: an SBW25-specific virus. These findings suggest that evolution occurring over ecological time scales can be a key driver of the structure of natural microbial communities, particularly in situations where some species have an evolutionary head start following large perturbations, such as exposure to antibiotics or crop planting and harvesting.

Microbial ecology studies are often performed through extraction of metagenomic DNA followed by amplification and sequencing of a marker. It is known that each step may bias the results. These biases have been explored for the study of bacterial communities, but rarely for fungi. Our aim was therefore to evaluate methods for the study of the gut mycobiome. We first evaluated DNA extraction methods in fungal cultures relevant to the gut. Afterwards, to assess how these methods would behave with an actual sample, stool from a donor was spiked with cells from the same cultures. We found that different extraction kits favour some species and bias against others. In terms of amplicon sequencing, we evaluated five primer sets, two for ITS2 and one for ITS1, 18S and 28S rRNA. Results showed that 18S rRNA outperformed the other markers: it was able to amplify all the species in the mock community and to discriminate among them. ITS primers showed both amplification and sequencing biases, the latter related to the variable length of the product. We identified several biases in the characterisation of the gut mycobiome and showed how crucial it is to be aware of these before drawing conclusions from the results of these studies.