TRPV channel nanchung and TRPA channel water witch form insecticide-activated complexes.

We have previously shown that insect vanilloid-type transient receptor potential (TRPV) channels Nanchung (Nan) and Inactive (Iav) form complexes, which can be over-stimulated and eventually silenced by commercial insecticides, afidopyropen, pymetrozine and pyrifluquinazon. Silencing of the TRPV channels by the insecticides perturbs function of the mechano-sensory organs, chordotonal organs, disrupting sound perception, gravitaxis, and feeding. In addition to TRPV channels, chordotonal organs express an ankyrin-type transient receptor potential (TRPA) channel, Water witch (Wtrw). Genetic data implicate Wtrw in sound and humidity sensing, although the signaling pathway, which links Wtrw to these functions has not been clearly defined. Here we show that, in heterologous system, Nan and Wtrw form calcium channels, which can be activated by afidopyropen, pymetrozine and an endogenous agonist, nicotinamide. Analogous to Nan-Iav heteromers, Nan forms the main binding interface for afidopyropen, whereas co-expression of Wtrw dramatically increases its binding affinity. Pymetrozine competes with afidopyropen for binding to Nan-Wtrw complexes, suggesting that these compounds have overlapping binding sites. Analysis of Drosophila single-nucleus transcriptomic atlas revealed co-expression of nan and wtrw in audio- and mechanosensory neurons. The observation that Nan can form insecticide-sensitive heteromers with more than one type of TRP channels, raises a possibility that Nan may partner with some other TRP channel(s). In addition, we show that Wtrw can be activated by plant-derived reactive electrophiles, allyl isothiocyanate and cinnamaldehyde, defining new molecular target for these repellents.

Antibiotics-induced monodominance of a novel gut bacterial order.

OBJECTIVE: The composition of the healthy human adult gut microbiome is relatively stable over prolonged periods, and representatives of the most highly abundant and prevalent species have been cultured and described. However, microbial abundances can change on perturbations, such as antibiotics intake, enabling the identification and characterisation of otherwise low abundant species. DESIGN: Analysing gut microbial time-series data, we used shotgun metagenomics to create strain level taxonomic and functional profiles. Community dynamics were modelled postintervention with a focus on conditionally rare taxa and previously unknown bacteria. RESULTS: In response to a commonly prescribed cephalosporin (ceftriaxone), we observe a strong compositional shift in one subject, in which a previously unknown species, UBorkfalki ceftriaxensis, was identified, blooming to 92% relative abundance. The genome assembly reveals that this species (1) belongs to a so far undescribed order of Firmicutes, (2) is ubiquitously present at low abundances in at least one third of adults, (3) is opportunistically growing, being ecologically similar to typical probiotic species and (4) is stably associated to healthy hosts as determined by single nucleotide variation analysis. It was the first coloniser after the antibiotic intervention that led to a long-lasting microbial community shift and likely permanent loss of nine commensals. CONCLUSION: The bloom of UB. ceftriaxensis and a subsequent one of Parabacteroides distasonis demonstrate the existence of monodominance community states in the gut. Our study points to an undiscovered wealth of low abundant but common taxa in the human gut and calls for more highly resolved longitudinal studies, in particular on ecosystem perturbations.

Metagenomic analysis of gut microbial communities from a Central Asian population.

OBJECTIVE: Changes in the gut microbiota are increasingly recognised to be involved in many diseases. This ecosystem is known to be shaped by many factors, including climate, geography, host nutrition, lifestyle and medication. Thus, knowledge of varying populations with different habits is important for a better understanding of the microbiome. DESIGN: We therefore conducted a metagenomic analysis of intestinal microbiota from Kazakh donors, recruiting 84 subjects, including male and female healthy subjects and metabolic syndrome (MetS) patients aged 25-75 years, from the Kazakh administrative centre, Astana. We characterise and describe these microbiomes, the first deep-sequencing cohort from Central Asia, in comparison with a global dataset (832 individuals from five countries on three continents), and explore correlations between microbiota, clinical and laboratory parameters as well as with nutritional data from Food Frequency Questionnaires. RESULTS: We observe that Kazakh microbiomes are relatively different from both European and East Asian counterparts, though similar to other Central Asian microbiomes, with the most striking difference being significantly more samples falling within the Prevotella-rich enterotype, potentially reflecting regional diet and lifestyle. We show that this enterotype designation remains stable within an individual over time in 82% of cases. We further observe gut microbiome features that distinguish MetS patients from controls (eg, significantly reduced Firmicutes to Bacteroidetes ratio, Bifidobacteria and Subdoligranulum, alongside increased Prevotella), though these overlap little with previously published reports and thus may reflect idiosyncrasies of the present cohort. CONCLUSION: Taken together, this exploratory study describes gut microbiome data from an understudied population, providing a starting point for further comparative work on biogeography and research on widespread diseases. TRIAL REGISTRATION NUMBER: ISRCTN37346212; Post-results.

Similarity of the dog and human gut microbiomes in gene content and response to diet.

BACKGROUND: Gut microbes influence their hosts in many ways, in particular by modulating the impact of diet. These effects have been studied most extensively in humans and mice. In this work, we used whole genome metagenomics to investigate the relationship between the gut metagenomes of dogs, humans, mice, and pigs. RESULTS: We present a dog gut microbiome gene catalog containing 1,247,405 genes (based on 129 metagenomes and a total of 1.9 terabasepairs of sequencing data). Based on this catalog and taxonomic abundance profiling, we show that the dog microbiome is closer to the human microbiome than the microbiome of either pigs or mice. To investigate this similarity in terms of response to dietary changes, we report on a randomized intervention with two diets (high-protein/low-carbohydrate vs. lower protein/higher carbohydrate). We show that diet has a large and reproducible effect on the dog microbiome, independent of breed or sex. Moreover, the responses were in agreement with those observed in previous human studies. CONCLUSIONS: We conclude that findings in dogs may be predictive of human microbiome results. In particular, a novel finding is that overweight or obese dogs experience larger compositional shifts than lean dogs in response to a high-protein diet.

metaSNV: A tool for metagenomic strain level analysis.

We present metaSNV, a tool for single nucleotide variant (SNV) analysis in metagenomic samples, capable of comparing populations of thousands of bacterial and archaeal species. The tool uses as input nucleotide sequence alignments to reference genomes in standard SAM/BAM format, performs SNV calling for individual samples and across the whole data set, and generates various statistics for individual species including allele frequencies and nucleotide diversity per sample as well as distances and fixation indices across samples. Using published data from 676 metagenomic samples of different sites in the oral cavity, we show that the results of metaSNV are comparable to those of MIDAS, an alternative implementation for metagenomic SNV analysis, while data processing is faster and has a smaller storage footprint. Moreover, we implement a set of distance measures that allow the comparison of genomic variation across metagenomic samples and delineate sample-specific variants to enable the tracking of specific strain populations over time. The implementation of metaSNV is available at: http://metasnv.embl.de/.

Visualization and analysis of gene expression in tissue sections by spatial transcriptomics.

Analysis of the pattern of proteins or messengerRNAs (mRNAs) in histological tissue sections is a cornerstone in biomedical research and diagnostics. This typically involves the visualization of a few proteins or expressed genes at a time. We have devised a strategy, which we call "spatial transcriptomics," that allows visualization and quantitative analysis of the transcriptome with spatial resolution in individual tissue sections. By positioning histological sections on arrayed reverse transcription primers with unique positional barcodes, we demonstrate high-quality RNA-sequencing data with maintained two-dimensional positional information from the mouse brain and human breast cancer. Spatial transcriptomics provides quantitative gene expression data and visualization of the distribution of mRNAs within tissue sections and enables novel types of bioinformatics analyses, valuable in research and diagnostics.

Disentangling type 2 diabetes and metformin treatment signatures in the human gut microbiota.

In recent years, several associations between common chronic human disorders and altered gut microbiome composition and function have been reported. In most of these reports, treatment regimens were not controlled for and conclusions could thus be confounded by the effects of various drugs on the microbiota, which may obscure microbial causes, protective factors or diagnostically relevant signals. Our study addresses disease and drug signatures in the human gut microbiome of type 2 diabetes mellitus (T2D). Two previous quantitative gut metagenomics studies of T2D patients that were unstratified for treatment yielded divergent conclusions regarding its associated gut microbial dysbiosis. Here we show, using 784 available human gut metagenomes, how antidiabetic medication confounds these results, and analyse in detail the effects of the most widely used antidiabetic drug metformin. We provide support for microbial mediation of the therapeutic effects of metformin through short-chain fatty acid production, as well as for potential microbiota-mediated mechanisms behind known intestinal adverse effects in the form of a relative increase in abundance of Escherichia species. Controlling for metformin treatment, we report a unified signature of gut microbiome shifts in T2D with a depletion of butyrate-producing taxa. These in turn cause functional microbiome shifts, in part alleviated by metformin-induced changes. Overall, the present study emphasizes the need to disentangle gut microbiota signatures of specific human diseases from those of medication.

TagGD: fast and accurate software for DNA Tag generation and demultiplexing.

Multiplexing is of vital importance for utilizing the full potential of next generation sequencing technologies. We here report TagGD (DNA-based Tag Generator and Demultiplexor), a fully-customisable, fast and accurate software package that can generate thousands of barcodes satisfying user-defined constraints and can guarantee full demultiplexing accuracy. The barcodes are designed to minimise their interference with the experiment. Insertion, deletion and substitution events are considered when designing and demultiplexing barcodes. 20,000 barcodes of length 18 were designed in 5 minutes and 2 million barcoded Illumina HiSeq-like reads generated with an error rate of 2% were demultiplexed with full accuracy in 5 minutes. We believe that our software meets a central demand in the current high-throughput biology and can be utilised in any field with ample sample abundance. The software is available on GitHub (https://github.com/pelinakan/UBD.git).

Comprehensive analysis of the genome transcriptome and proteome landscapes of three tumor cell lines.

We here present a comparative genome, transcriptome and functional network analysis of three human cancer cell lines (A431, U251MG and U2OS), and investigate their relation to protein expression. Gene copy numbers significantly influenced corresponding transcript levels; their effect on protein levels was less pronounced. We focused on genes with altered mRNA and/or protein levels to identify those active in tumor maintenance. We provide comprehensive information for the three genomes and demonstrate the advantage of integrative analysis for identifying tumor-related genes amidst numerous background mutations by relating genomic variation to expression/protein abundance data and use gene networks to reveal implicated pathways.