Notes

Report with the RNA inside exosomes from astrocytes as well as microglia utilizing strong sequencing: effects regarding neurodegeneration mechanisms.
Bacteria respond to changes in their environment with specific transcriptional programmes, but even within genetically identical populations these programmes are not homogenously expressed1. Such transcriptional heterogeneity between individual bacteria allows genetically clonal communities to develop a complex array of phenotypes1, examples of which include persisters that resist antibiotic treatment and metabolically specialized cells that emerge under nutrient-limiting conditions2. Fluorescent reporter constructs have played a pivotal role in deciphering heterogeneous gene expression within bacterial populations3 but have been limited to recording the activity of single genes in a few genetically tractable model species, whereas the vast majority of bacteria remain difficult to engineer and/or even to cultivate. Single-cell transcriptomics is revolutionizing the analysis of phenotypic cell-to-cell variation in eukaryotes, but technical hurdles have prevented its robust application to prokaryotes. Here, using an improved poly(A)-independent single-cell RNA-sequencing protocol, we report the faithful capture of growth-dependent gene expression patterns in individual Salmonella and Pseudomonas bacteria across all RNA classes and genomic regions. These transcriptomes provide important reference points for single-cell RNA-sequencing of other bacterial species, mixed microbial communities and host-pathogen interactions.Sensing of microbes activates the innate immune system, depending on functional mitochondria. However, pathogenic bacteria inhibit mitochondrial activity by delivering toxins via outer membrane vesicles (OMVs). How macrophages respond to pathogenic microbes that target mitochondria remains unclear. Here, we show that macrophages exposed to OMVs from Neisseria gonorrhoeae, uropathogenic Escherichia coli and Pseudomonas aeruginosa induce mitochondrial apoptosis and NLRP3 inflammasome activation. OMVs and toxins that cause mitochondrial dysfunction trigger inhibition of host protein synthesis, which depletes the unstable BCL-2 family member MCL-1 and induces BAK-dependent mitochondrial apoptosis. In parallel with caspase-11-mediated pyroptosis, mitochondrial apoptosis and potassium ion efflux activate the NLRP3 inflammasome after OMV exposure in vitro. Importantly, in the in vivo setting, the activation and release of interleukin-1β in response to N. gonorrhoeae OMVs is regulated by mitochondrial apoptosis. Our data highlight how innate immune cells sense infections by monitoring mitochondrial health.The IncC family of broad-host-range plasmids enables the spread of antibiotic resistance genes among human enteric pathogens1-3. Although aspects of IncC plasmid conjugation have been well studied4-9, many roles of conjugation genes have been assigned based solely on sequence similarity. We applied hypersaturated transposon mutagenesis and transposon-directed insertion-site sequencing to determine the set of genes required for IncC conjugation. We identified 27 conjugation genes, comprising 19 that were previously identified (including two regulatory genes, acaDC) and eight not previously associated with conjugation. We show that one previously unknown gene, acaB, encodes a transcriptional regulator that has a crucial role in the regulation of IncC conjugation. AcaB binds upstream of the acaDC promoter to increase acaDC transcription; in turn, AcaDC activates the transcription of IncC conjugation genes. We solved the crystal structure of AcaB at 2.9-Å resolution and used this to guide functional analyses that reveal how AcaB binds to DNA. This improved understanding of IncC conjugation provides a basis for the development of new approaches to reduce the spread of these multi-drug-resistance plasmids.In recent years, there has been a growing interest in understanding the relationship between sleep and suicide. Although sleep disturbances are commonly cited as critical risk factors for suicidal thoughts and behaviours, it is unclear to what degree sleep disturbances confer risk for suicide. The aim of this meta-analysis was to clarify the extent to which sleep disturbances serve as risk factors (i.e., longitudinal correlates) for suicidal thoughts and behaviours. Our analyses included 156 total effects drawn from 42 studies published between 1982 and 2019. We used a random effects model to analyse the overall effects of sleep disturbances on suicidal ideation, attempts, and death. We additionally explored potential moderators of these associations. Our results indicated that sleep disturbances are statistically significant, yet weak, risk factors for suicidal thoughts and behaviours. The strongest associations were found for insomnia, which significantly predicted suicide ideation (OR 2.10 [95% CI 1.83-2.41]), and nightmares, which significantly predicted suicide attempt (OR 1.81 [95% CI 1.12-2.92]). Given the low base rate of suicidal behaviours, our findings raise questions about the practicality of relying on sleep disturbances as warning signs for imminent suicide risk. Future research is necessary to uncover the causal mechanisms underlying the relationship between sleep disturbances and suicide.With the growth of metabolomics research, more and more studies are conducted on large numbers of samples. Due to technical limitations of the Liquid Chromatography-Mass Spectrometry (LC/MS) platform, samples often need to be processed in multiple batches. https://www.selleckchem.com/products/pkm2-inhibitor-compound-3k.html Across different batches, we often observe differences in data characteristics. In this work, we specifically focus on data generated in multiple batches on the same LC/MS machinery. Traditional preprocessing methods treat all samples as a single group. Such practice can result in errors in the alignment of peaks, which cannot be corrected by post hoc application of batch effect correction methods. In this work, we developed a new approach that address the batch effect issue in the preprocessing stage, resulting in better peak detection, alignment and quantification. It can be combined with down-stream batch effect correction methods to further correct for between-batch intensity differences. The method is implemented in the existing workflow of the apLCMS platform.
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