Estimating variation within the genes and inferring the phylogeny of 186 sequenced diverse Escherichia coli genomes

<p>Abstract</p> <p>Background</p> <p><it>Escherichia coli</it> exists in commensal and pathogenic forms. By measuring the variation of individual genes across more than a hundred sequenced genomes, gene variation can be studied in detail, including the number of mutations found for any given gene. This knowledge will be useful for creating better phylogenies, for determination of molecular clocks and for improved typing techniques.</p> <p>Results</p> <p>We find 3,051 gene clusters/families present in at least 95% of the genomes and 1,702 gene clusters present in 100% of the genomes. The former 'soft core' of about 3,000 gene families is perhaps more biologically relevant, especially considering that many of these genome sequences are draft quality. The <it>E. coli</it> pan-genome for this set of isolates contains 16,373 gene clusters.</p> <p>A core-gene tree, based on alignment and a pan-genome tree based on gene presence/absence, maps the relatedness of the 186 sequenced <it>E. coli</it> genomes. The core-gene tree displays high confidence and divides the <it>E. coli</it> strains into the observed MLST type clades and also separates defined phylotypes.</p> <p>Conclusion</p> <p>The results of comparing a large and diverse <it>E. coli</it> dataset support the theory that reliable and good resolution phylogenies can be inferred from the core-genome. The results further suggest that the resolution at the isolate level may, subsequently be improved by targeting more variable genes. The use of whole genome sequencing will make it possible to eliminate, or at least reduce, the need for several typing steps used in traditional epidemiology.</p

snpTree - a web-server to identify and construct SNP trees from whole genome sequence data.

<p>Abstract</p> <p>Background</p> <p>The advances and decreasing economical cost of whole genome sequencing (WGS), will soon make this technology available for routine infectious disease epidemiology. In epidemiological studies, outbreak isolates have very little diversity and require extensive genomic analysis to differentiate and classify isolates. One of the successfully and broadly used methods is analysis of single nucletide polymorphisms (SNPs). Currently, there are different tools and methods to identify SNPs including various options and cut-off values. Furthermore, all current methods require bioinformatic skills. Thus, we lack a standard and simple automatic tool to determine SNPs and construct phylogenetic tree from WGS data.</p> <p>Results</p> <p>Here we introduce snpTree, a server for online-automatic SNPs analysis. This tool is composed of different SNPs analysis suites, perl and python scripts. snpTree can identify SNPs and construct phylogenetic trees from WGS as well as from assembled genomes or contigs. WGS data in fastq format are aligned to reference genomes by BWA while contigs in fasta format are processed by Nucmer. SNPs are concatenated based on position on reference genome and a tree is constructed from concatenated SNPs using FastTree and a perl script. The online server was implemented by HTML, Java and python script.</p> <p>The server was evaluated using four published bacterial WGS data sets (<it>V. cholerae</it>, <it>S. aureus </it>CC398, <it>S</it>. Typhimurium and <it>M. tuberculosis</it>). The evalution results for the first three cases was consistent and concordant for both raw reads and assembled genomes. In the latter case the original publication involved extensive filtering of SNPs, which could not be repeated using snpTree.</p> <p>Conclusions</p> <p>The snpTree server is an easy to use option for rapid standardised and automatic SNP analysis in epidemiological studies also for users with limited bioinformatic experience. The web server is freely accessible at http://www.cbs.dtu.dk/services/snpTree-1.0/.</p

Actinobacillus pleruropneumoniae transcriptome analysis during early infection - coping with a hostile environment

Aim: To obtain an increased understanding of how the porcine lung pathogen Actinobacillus pleuropneumoniae (Ap) establish infection in the host. Understanding the means by which a pathogen establishes and maintains infection in the host organism is the first step towards controlling disease. Methods: The local in vivo genetic response of Ap during the early phase of infection in porcine lungs was detailed using pangenomic microarray analysis. The global transcriptional patterns of Ap serotype 2 and 6 isolated from lung tissue biopsies of 25 experimentally infected pigs were compared at four time points between 6 and 48 hours post infection. Results: We identified 310 genes (p &lt;1.0 × 10-8) that were differentially expressed during the first 48 hours of infection. Most of these genes appeared to be up-regulated at 6 hours post inoculation after which the expression gradually declined over the next 42 hours. Functional analysis identified a number of putative virulence genes to be initially up-regulated. Conclusions: This is the first study monitoring the development of Ap response in the porcine host during early infection. The ability of pathogenic bacteria to adjust gene expression in response to environmental stimuli is critical for bacterial survival within the host. The genes identified as differentially expressed in this study may represent a core set of genes which are mobilized to cope with the host immune response and adapt to the hostile environment. The potential virulence genes identified may represent valuable candidates for vaccine development

Lack of Neuronal IFN-β-IFNAR Causes Lewy Body- and Parkinson's Disease-like Dementia.

Neurodegenerative diseases have been linked to inflammation, but whether altered immunomodulation plays a causative role in neurodegeneration is not clear. We show that lack of cytokine interferon-β (IFN-β) signaling causes spontaneous neurodegeneration in the absence of neurodegenerative disease-causing mutant proteins. Mice lacking Ifnb function exhibited motor and cognitive learning impairments with accompanying α-synuclein-containing Lewy bodies in the brain, as well as a reduction in dopaminergic neurons and defective dopamine signaling in the nigrostriatal region. Lack of IFN-β signaling caused defects in neuronal autophagy prior to α-synucleinopathy, which was associated with accumulation of senescent mitochondria. Recombinant IFN-β promoted neurite growth and branching, autophagy flux, and α-synuclein degradation in neurons. In addition, lentiviral IFN-β overexpression prevented dopaminergic neuron loss in a familial Parkinson's disease model. These results indicate a protective role for IFN-β in neuronal homeostasis and validate Ifnb mutant mice as a model for sporadic Lewy body and Parkinson's disease dementia.Support to S.I.-N. was from Danish Council For Independent Research (DFF)-Medical Sciences, Alzheimer-forskningsfonden, Danish Multiple Sclerosis Society, Danish Cancer Society and Lundbeck Foundation. D.C.R. is a Wellcome Trust Principal Research Fellow.This is the final version of the article. It first appeared from Elsevier via http://dx.doi.org/10.1016/j.cell.2015.08.06

<i>SNHG5</i> promotes colorectal cancer cell survival by counteracting STAU1-mediated mRNA destabilization

We currently have limited knowledge of the involvement of long non-coding RNAs (lncRNAs) in normal cellular processes and pathologies. Here, we identify and characterize SNHG5 as a stable cytoplasmic lncRNA with up-regulated expression in colorectal cancer. Depletion of SNHG5 induces cell cycle arrest and apoptosis in vitro and limits tumour outgrowth in vivo, whereas SNHG5 overexpression counteracts oxaliplatin-induced apoptosis. Using an unbiased approach, we identify 121 transcript sites interacting with SNHG5 in the cytoplasm. Importantly, knockdown of key SNHG5 target transcripts, including SPATS2, induces apoptosis and thus mimics the effect seen following SNHG5 depletion. Mechanistically, we suggest that SNHG5 stabilizes the target transcripts by blocking their degradation by STAU1. Accordingly, depletion of STAU1 rescues the apoptosis induced after SNHG5 knockdown. Hence, we characterize SNHG5 as a lncRNA promoting tumour cell survival in colorectal cancer and delineate a novel mechanism in which a cytoplasmic lncRNA functions through blocking the action of STAU1

SWI/SNF Subunits SMARCA4, SMARCD2 and DPF2 Collaborate in MLL-Rearranged Leukaemia Maintenance

<div><p>Alterations in chromatin structure caused by deregulated epigenetic mechanisms collaborate with underlying genetic lesions to promote cancer. SMARCA4/BRG1, a core component of the SWI/SNF ATP-dependent chromatin-remodelling complex, has been implicated by its mutational spectrum as exerting a tumour-suppressor function in many solid tumours; recently however, it has been reported to sustain leukaemogenic transformation in MLL-rearranged leukaemia in mice. Here we further explore the role of SMARCA4 and the two SWI/SNF subunits SMARCD2/BAF60B and DPF2/BAF45D in leukaemia. We observed the selective requirement for these proteins for leukaemic cell expansion and self-renewal <i>in-vitro</i> as well as in leukaemia. Gene expression profiling in human cells of each of these three factors suggests that they have overlapping functions in leukaemia. The gene expression changes induced by loss of the three proteins demonstrate that they are required for the expression of haematopoietic stem cell associated genes but in contrast to previous results obtained in mouse cells, the three proteins are not required for the expression of c-MYC regulated genes.</p></div

No changes in <i>MYC</i> expression after SMARCA4 knockdown.

<p>(A-B) GSEA plots showing enrichment of the indicated gene sets in genes ranked by Signal2noise metric in SMARCA4 knockdown versus control THP-1 cells. (C) Top: schematic representation of the human <i>MYC</i> locus; coding sequence is marked in green; qPCR primer pairs and their amplification products are represented as regions 1–4. Bottom: Relative mRNA levels of <i>MYC</i> assessed by using the indicated primer pairs. (D-F) Myc rescue experiments of Smarca4 and Jmjd1c KD in MLL-AF9 cells. (D-E) Relative mRNA levels of the indicated genes in cells transduced with the indicated combinations of vectors. e, empty vector; Myc, Myc expression vector; shScr, non-targeting control pMLS-YFP vector; shJmjd1c and shSmarca4, pMLS-YFP vectors targeting Jmjd1c and Smarca4, respectively. (F) Normalised ratios of GFP⁺ YFP⁺ cell percentages between shJmjd1c and shScr samples (left) or shSmarca4 and shScr samples (right) plotted over an 8-day time course starting from day 2 after transduction.</p

Gene expression changes upon knockdown of single SWI/SNF complex components in human AML cells.

<p>(A and B) Venn diagrams showing overlap of genes significantly changing down (A), or up (B) (p<0.05) upon SMARCA4, SMARCD2 and DPF2 knockdown in THP-1 cells. (C-D) Gene set enrichment analysis (GSEA) plots showing enrichment of indicated gene sets in genes ranked by Signal2noise metric in SMARCA4 or DPF2 knock-down versus control THP-1 cells.</p

Depletion of single SWI/SNF complex components inhibits AML maintenance.

<p>(A) Mouse MLL-AF9 cell number fold change between day 5 and day 9 after transduction. Cells were transduced with pLKO constructs expressing the indicated shRNAs and selected with puromycin from day 2 after transduction. (B) Relative mRNA levels of <i>Smarca4</i>, <i>Smarcd2</i> and <i>Dpf2</i> in MEFs transduced with the indicated pLKO constructs. (C) Number of colonies generated by MLL-AF9 cells transduced with the indicated pMLS vectors. (D) Relative mRNA levels of <i>Smarca4</i>, <i>Smarcd2</i> and <i>Dpf2</i> in MEFs transduced with the indicated pMLS vectors. (E) Rescue experiments. MLL-AF9 cells were co-transduced with pMLS-YFP carrying shScr, shSmarca4, shSmarcd2 or shDpf2, as indicated, and control pMIGRI (pMIG-Stuffer) or pMIGRI expressing human <i>SMARCA4</i>, <i>SMARCD2</i> or <i>DPF2</i> cDNA. Normalized ratios of GFP⁺/YFP⁺ cell percentages between shSmarca4, shSmarcd2 or shDpf2 and shScr samples are plotted over a time course starting from day 2 after transduction. (F) Competitive proliferation assay of c-Kit-enriched mouse bone marrow cells transduced with the indicated pMLS vectors. (G) Left: Number of colonies generated by LSK cells transduced with the indicated pLKO shRNAs. Right: Absolute numbers of LSK cells with indicated knockdown in liquid culture. (H) Survival curves of sublethally irradiated mice transplanted with 10⁴ MLL-AF9 cells transduced with the indicated pMLS vectors.</p