A microRNA feedback loop regulates global microRNA abundance during aging

Expression levels of many microRNAs (miRNAs) change during aging, notably declining globally in a number of organisms and tissues across taxa. However, little is known about the mechanisms or the biological relevance for this change. We investigated the network of genes that controls miRNA transcription and processing during C. elegans aging. We found that miRNA biogenesis genes are highly networked with transcription factors and aging-associated miRNAs. In particular, miR-71, known to influence life span and itself up-regulated during aging, represses alg-1/Argonaute expression post-transcriptionally during aging. Increased ALG-1 abundance in mir-71 loss-of-function mutants led to globally increased miRNA expression. Interestingly, these mutants demonstrated widespread mRNA expression dysregulation and diminished levels of variability both in gene expression and in overall life span. Thus, the progressive molecular decline often thought to be the result of accumulated damage over an organism's life may be partially explained by a miRNA-directed mechanism of age-associated decline.</jats:p

Dynamic expression of small non-coding RNAs, including novel microRNAs and piRNAs/21U-RNAs, during Caenorhabditis elegans development

A deep-sequencing approach to profiling gender-specific developmental regulation of small non-coding RNA expression in C. elegans reveals dynamic temporal expression and novel miRNAs and 21U RNAs

High Genetic Diversity among Community-Associated Staphylococcus aureus in Europe: Results from a Multicenter Study

Background: Several studies have addressed the epidemiology of community-associated Staphylococcus aureus (CA-SA) in Europe; nonetheless, a comprehensive perspective remains unclear. In this study, we aimed to describe the population structure of CA-SA and to shed light on the origin of methicillin-resistant S. aureus (MRSA) in this continent. Methods and Findings: A total of 568 colonization and infection isolates, comprising both MRSA and methicillin-susceptible S. aureus (MSSA), were recovered in 16 European countries, from community and community-onset infections. The genetic background of isolates was characterized by molecular typing techniques (spa typing, pulsed-field gel electrophoresis and multilocus sequence typing) and the presence of PVL and ACME was tested by PCR. MRSA were further characterized by SCCmec typing. We found that 59 % of all isolates were associated with community-associated clones. Most MRSA were related with USA300 (ST8-IVa and variants) (40%), followed by the European clone (ST80-IVc and derivatives) (28%) and the Taiwan clone (ST59-IVa and related clonal types) (15%). A total of 83 % of MRSA carried Panton-Valentine leukocidin (PVL) and 14 % carried the arginine catabolic mobile element (ACME). Surprisingly, we found a high genetic diversity among MRSA clonal types (ST-SCCmec), Simpson’s index of diversity = 0.852 (0.788–0.916). Specifically, about half of the isolates carried novel associations between genetic background and SCCmec. Analysis by BURP showed that some CA-MSSA and CA-MRS

Novel MicroRNAs Differentially Expressed during Aging in the Mouse Brain

<div><p>MicroRNAs (miRNAs) are endogenous small RNA molecules that regulate gene expression post-transcriptionally. Work in <em>Caenorhabditis elegans</em> has shown that specific miRNAs function in lifespan regulation and in a variety of age-associated pathways, but the roles of miRNAs in the aging of vertebrates are not well understood. We examined the expression of small RNAs in whole brains of young and old mice by deep sequencing and report here on the expression of 558 known miRNAs and identification of 41 novel miRNAs. Of these miRNAs, 75 known and 18 novel miRNAs exhibit greater than 2.0-fold expression changes. The majority of expressed miRNAs in our study decline in relative abundance in the aged brain, in agreement with trends observed in other miRNA studies in aging tissues and organisms. Target prediction analysis suggests that many of our novel aging-associated miRNAs target genes in the insulin signaling pathway, a central node of aging-associated genetic networks. These novel miRNAs may thereby regulate aging-related functions in the brain. Since many mouse miRNAs are conserved in humans, the aging-affected brain miRNAs we report here may represent novel regulatory genes that also function during aging in the human brain.</p> </div

Brain-expressed miRNAs.

<p>(<b>a</b>) Expression changes of miRNAs in mouse brain with aging. Distribution of individual miRNA expression changes are ranked by those miRNAs that exhibit the greatest increase in expression with aging (log2 Ratio (Old/Young)). Blue: known miRNAs, red: novel miRNAs. (<b>b</b>) Known miRNAs that change more than 2.0-fold in expression in old versus young mouse brains. Only miRNAs with at least 10 sequence reads at one time point are shown and P-value <0.05 are in bold. MiRNA frequency was normalized by all reads that matched to the mouse genome (mm9) (Old/Young  = 1.472107). (<b>c</b>) Comparison of qRT-PCR data with deep sequencing data for three known miRNAs. Values shown are fold changes in old versus young brain expression levels. qPCR results were normalized to U6 snRNA expression levels; error bars indicate standard deviation for technical triplicate. Statistically significant difference from U6 control denoted by asterisks (*: two-tailed P-value <0.01).</p

Genomic locations of brain-expressed miRNAs found within 10 kb region of each other.

<p>(a) miRNA clusters found on chromosome 12. Top panel: location of miRNAs relative to the entire chromosome 12. Bottom three panels: three distinct clusters found in the boxed off region in the top panel. The three clusters are located close to each other. (b) miRNA clusters found on X chromosome. Top panel: location of miRNAs relative to the entire X chromosome. Bottom two panels: clusters found in the boxed off regions of the top panel, from left to right. Blue: brain-expressed known miRNAs. Teal: miRNAs downregulated in expression in our dataset. Purple: miRNAs upregulated in expression in our dataset. Pink: known miRNAs found in the same genomic region but not found to be expressed in our dataset. Red: brain-expressed novel miRNA candidates. Green: TROMER transcriptome data (retrieved from UCSC Genome Browser).</p

Novel miRNA candidates.

<p>(<b>a</b>) Novel miRNA candidates that change more than 2.0-fold in expression in old versus young mouse brains. MiRNA frequency was normalized by all reads that matched to the mouse genome (mm9) (Old/Young  = 1.472107). †: candidates validated by qRT-PCR. ‡: candidates with sequence overlap with known miRNAs (but have distinct mature miRNA sequences: isomiRs). ^A: Novel miRNA candidates that map to regions overlapping snoRNA and rRNA sequences (see main text). Blue font: miRNA novel to mouse. Black font: completely novel miRNA sequence, excluding seed sequence matches. Differentially expressed miRNAs with P-values <0.05 (calculated using DEGseq <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0040028#pone.0040028-Wang1" target="_blank">[20]</a>) indicated in bold. See also <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0040028#pone.0040028.s005" target="_blank">Table S4</a>. (<b>b</b>) Secondary structures of putative precursor hairpins corresponding to nine novel miRNA candidates identified in this study. The predicted miRNA mature sequences are highlighted in red. Four of these novel miRNAs were found to be up-regulated (top) in aged mouse brain while five others were down-regulated (bottom). (See also <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0040028#pone.0040028.s005" target="_blank">Table S4</a>). (<b>c</b>) Comparison of qRT-PCR data with deep sequencing data for the nine novel miRNA candidates shown in (b). Values shown are log2 ratios of old versus young brain expression levels. qPCR results were normalized to U6 snRNA expression levels. Inset: Correlation of expression changes as measured by deep sequencing versus qPCR (Pearson correlation coefficient  = 0.78). Plot for miR-5620 (isomiR) was taken out as the sequence was not reliably detected by qPCR. (<b>d</b>) Sequence alignment of novel miRNA candidates with known miRNAs of other species. *: conserved nucleotide. age: <i>Ateles geoffroyi</i>. bta: <i>Bos taurus</i>. dan: <i>Drosophila ananassae</i>. der: <i>Drosophila erecta</i>. dgr: <i>Drosophila grimshawi</i>. dme: <i>Drosophila melanogaster</i>. dmo: <i>Drosophila mojavensis</i>. dpe: <i>Drosophila persimilis</i>. dps: <i>Drosophila pseudoobscura</i>. dse: <i>Drosophila sechellia</i>. dsi: <i>Drosophila simulans</i>. dvi: <i>Drosophila virilis</i>. dwi: <i>Drosophila willistoni</i>. dya: <i>Drosophila yakuba</i>. gga: <i>Gallus gallus</i>. ggo: <i>Gorilla gorilla</i>. hsa: <i>Homo sapiens</i>. lla: <i>Lagothrix lagotricha</i>. mdo: <i>Monodelphis domestica</i>. mml: <i>Macaca mulatta</i>. mne: <i>Macaca nemestrina</i>. ppa: <i>Pan paniscus</i>. ppy: <i>Pongo pygmaeus</i>. ptr: <i>Pan troglodytes</i>. rno: <i>Rattus norvegicus</i>. sla: <i>Saguinus labiatus</i>. sme: <i>Schmidtea mediterranea.</i> xtr: <i>Xenopus tropicalis</i>.</p

The insulin signaling pathway is predicted to be targeted by many aging-regulated novel miRNA candidates.

<p>Multiple novel miRNA candidates are predicted to target each of the genes implicated in the pathway, and each novel miRNA candidate is predicted to target multiple genes in the pathway. In red: upregulated (>2.0-fold) novel miRNAs; in black: downregulated (>2.0-fold) novel miRNAs.</p

Evidence for the dissemination to humans of Methicillin-Resistant Staphylococcus aureus ST398 through the pork production chain : a study in a portuguese slaughterhouse

Research Areas: MicrobiologyLivestock-associated methicillin-resistant Staphylococcus aureus (LA-MRSA) ST398 was recovered from infections in humans exposed to animals, raising public health concerns. However, contact with food producing chain as a means of transmission of LA-MRSA to humans remains poorly understood. We aimed to assess if pork production chain is a source of MRSA ST398 for human colonization and infection. MRSA from live pigs, meat, the environment, and slaughterhouse workers were analyzed by Pulsed-Field Gel Electrophoresis (PFGE), spa, MLST typing, SNPs and for antibiotic resistance and virulence gene profiles. We compared core and accessory genomes of MRSA ST398 isolated from slaughterhouse and hospital. We detected MRSA ST398 (t011, t108, t1451) along the entire pork production chain (live pigs: 60%; equipment: 38%; meat: 23%) and in workers (40%). All MRSA ST398 were multidrug resistant, and the majority carried genes encoding biocide resistance and enterotoxins. We found 23 cross-transmission events between live pigs, meat, and workers (6–55 SNPs). MRSA ST398 from infection and slaughterhouse environment belonged to the same clonal type (ST398, t011, SCCmec V), but differed in 321–378 SNPs. Pork production chain can be a source of MRSA ST398 for colonization of human slaughterhouse workers, which can represent a risk of subsequent meat contamination and human infection.info:eu-repo/semantics/publishedVersio

European external quality assessments for identification, molecular typing and characterization of Staphylococcus aureus

Objectives: We present the results of two European external quality assessments (EQAs) conducted in 2014 and 2016 under the auspices of the Study Group on Staphylococci and Staphylococcal Infections of ESCMID. The objective was to assess the performance of participating centres in characterizing Staphylococcus aureus using their standard in-house phenotypic and genotypic protocols. Methods: A total of 11 well-characterized blindly coded S. aureus (n = 9), Staphylococcus argenteus (n = 1) and Staphylococcus capitis (n = 1) strains were distributed to participants for analysis. Species identification, MIC determination, antimicrobial susceptibility testing, antimicrobial resistance and toxin gene detection and molecular typing including spa typing, SCCmec typing and MLST were performed. Results: Thirteen laboratories from 12 European countries participated in one EQA or both EQAs. Despite considerable diversity in the methods employed, good concordance (90%-100%) with expected results was obtained. Discrepancies were observed for: (i) identification of the S. argenteus strain; (ii) phenotypic detection of low-level resistance to oxacillin in the mecC-positive strain; (iii) phenotypic detection of the inducible MLSB strain; and (iv) WGS-based detection of some resistance and toxin genes. Conclusions: Overall, good concordance (90%-100%) with expected results was observed. In some instances, the accurate detection of resistance and toxin genes from WGS data proved problematic, highlighting the need for validated and internationally agreed-on bioinformatics pipelines before such techniques are implemented routinely by microbiology laboratories. We strongly recommend all national reference laboratories and laboratories acting as referral centres to participate in such EQA initiatives