A novel approach to sequence validating protein expression clones with automated decision making-1

<p><b>Copyright information:</b></p><p>Taken from "A novel approach to sequence validating protein expression clones with automated decision making"</p><p>http://www.biomedcentral.com/1471-2105/8/198</p><p>BMC Bioinformatics 2007;8():198-198.</p><p>Published online 13 Jun 2007</p><p>PMCID:PMC1914086.</p><p></p>ed number of discrepancies of each type. Different values can be set for discrepancies of low and high confidence. The user sets values for two thresholds – one that triggers a manual review, and one that automatically rejects the clone. Users can also opt to handle conservative and non-conservative amino acids substitutions separately or to treat all amino acid changes as one type. Once the settings are created, users can name the set and store it for future use. In this way, users may create different acceptance criteria for different purposes. Thus, a single collection of clones can be evaluated by different acceptance criteria by invoking these named sets. The criteria shown here are used routinely for determining final acceptance of clones. The numbers in the boxes indicate the absolute number of the indicated type of discrepancy for inclusion in that category. As indicated, this set of criteria does not distinguish between conservative and non-conservative missense mutations. Any clones with 1 or 0 high confidence missense substitution(s) are automatically accepted (as long as they have no other discrepancies that prevent automatic acceptance). Clones with 3 or more high-confidence missense substitutions are automatically rejected; if the clones have 2 they are triaged for additional sequencing or manual analysis. A higher bar is set to automatically reject clones based on low-confidence substitutions (10 or more), because many of these will be resolved with further sequencing. Similarly, this parameter set automatically passes clones only if they have no frameshift discrepancies of any type. Clones with 1 high-confidence or 9 low-confidence frameshift discrepancies or more are automatically rejected. Clones must meet all the pass criteria for automatic acceptance, whereas clones that meet any automatic fail criteria are automatically failed

A novel approach to sequence validating protein expression clones with automated decision making-3

<p><b>Copyright information:</b></p><p>Taken from "A novel approach to sequence validating protein expression clones with automated decision making"</p><p>http://www.biomedcentral.com/1471-2105/8/198</p><p>BMC Bioinformatics 2007;8():198-198.</p><p>Published online 13 Jun 2007</p><p>PMCID:PMC1914086.</p><p></p>ing process control and primer design are omitted from the figure for simplicity

Tandem Histone-Binding Domains Enhance the Activity of a Synthetic Chromatin Effector

Fusion proteins that specifically interact with biochemical marks on chromosomes represent a new class of synthetic transcriptional regulators that decode cell state information rather than DNA sequences. In multicellular organisms, information relevant to cell state, tissue identity, and oncogenesis is often encoded as biochemical modifications of histones, which are bound to DNA in eukaryotic nuclei and regulate gene expression states. We have previously reported the development and validation of the “polycomb-based transcription factor” (PcTF), a fusion protein that recognizes histone modifications through a protein–protein interaction between its polycomb chromodomain (PCD) motif and trimethylated lysine 27 of histone H3 (H3K27me3) at genomic sites. We demonstrated that PcTF activates genes at methyl-histone-enriched loci in cancer-derived cell lines. However, PcTF induces modest activation of a methyl-histone associated reporter compared to a DNA-binding activator. Therefore, we modified PcTF to enhance its binding avidity. Here, we demonstrate the activity of a modified regulator called Pc₂TF, which has two tandem copies of the H3K27me3-binding PCD at the N-terminus. Pc₂TF has a smaller apparent dissociation constant value <i>in vitro</i> and shows enhanced gene activation in HEK293 cells compared to PcTF. These results provide compelling evidence that the intrinsic histone-binding activity of the PCD motif can be used to tune the activity of synthetic histone-binding transcriptional regulators

Reduced Incidence of <i>Prevotella</i> and Other Fermenters in Intestinal Microflora of Autistic Children

<div><p>High proportions of autistic children suffer from gastrointestinal (GI) disorders, implying a link between autism and abnormalities in gut microbial functions. Increasing evidence from recent high-throughput sequencing analyses indicates that disturbances in composition and diversity of gut microbiome are associated with various disease conditions. However, microbiome-level studies on autism are limited and mostly focused on pathogenic bacteria. Therefore, here we aimed to define systemic changes in gut microbiome associated with autism and autism-related GI problems. We recruited 20 neurotypical and 20 autistic children accompanied by a survey of both autistic severity and GI symptoms. By pyrosequencing the V2/V3 regions in bacterial 16S rDNA from fecal DNA samples, we compared gut microbiomes of GI symptom-free neurotypical children with those of autistic children mostly presenting GI symptoms. Unexpectedly, the presence of autistic symptoms, rather than the severity of GI symptoms, was associated with less diverse gut microbiomes. Further, rigorous statistical tests with multiple testing corrections showed significantly lower abundances of the genera <i>Prevotella</i>, <i>Coprococcus</i>, and unclassified <i>Veillonellaceae</i> in autistic samples. These are intriguingly versatile carbohydrate-degrading and/or fermenting bacteria, suggesting a potential influence of unusual diet patterns observed in autistic children. However, multivariate analyses showed that autism-related changes in both overall diversity and individual genus abundances were correlated with the presence of autistic symptoms but not with their diet patterns. Taken together, autism and accompanying GI symptoms were characterized by distinct and less diverse gut microbial compositions with lower levels of <i>Prevotella</i>, <i>Coprococcus</i>, and unclassified <i>Veillonellaceae</i>.</p></div

Distribution of 39 subjects based on relative abundance.

<p>(A) The top 10 most abundant genera, (B) 4 most differentially abundant genera (Red-colored box: autistic children, blue-colored box: neurotypical children), (C) the genus <i>Prevotella</i> obtained by qPCR analysis (Red-colored box: autistic children, blue-colored box: neurotypical children), and (D) receiver operating characteristics (ROC) curve of the 4 genera that have the highest area under curve (AUC).</p

Correlations between the severity of GI problems and autism severity indices.

<p>Pearson and Spearman rank correlation between GI severity scores with autism severity (P1: Fisher transformation, P2: permutation test).</p

Genus level comparison of gut microbiota between neurotypical and autistic children.

<p>(A) Heat map profile and dendrogram of all identified genera (A01-A19: autistic children, N01–N20: neurotypical children). A red, orange, and blue scale bar represents scores of autistic symptoms, GI problems, and a log scale of the percentile abundance from a total bacteria, respectively. (B) Principal Component Analysis at the genus level from the autistic and neurotypical children. Blue- and red-, and black-colored dots represent neurotypical, autistic samples, and 16 selected genera, respectively. Three genera representing enterotypes (23) were identified in bold. (C) The gradient of <i>Prevotella</i> and <i>Bacteroides</i> through neurotypical and autistic children (*: P<0.05 by Mann-Whitney test).</p

Top 10 genera generating the highest area under curves in a receiver operating characteristics curve.

<p>A highest area under curves (AUC) value of 0.5 indicates no predictive, while an AUC of 1 indicates perfect ability to predict.</p

Comparison of gut microbiota within the genus <i>Prevotella</i> between neurotypical and autistic children.

<p>(A) Heat map profile and dendrogram (A01-A19: autistic children, N01–N20: neurotypical children). A red, orange, and blue scale bar represents scores of autistic symptoms, GI problems, and a log scale of the percentile abundance from a total bacteria, respectively. (B) Phylogenetic tree within the genus <i>Prevotella</i>. (C) The weighted UniFrac analysis with <i>Prevotella</i> copri-like 16 OTUs. Jackknife counts over 50 out of 100 are shown.</p

Comparison on bacterial richness and diversity between neurotypical and autistic children.

<p>(A) Rarefaction curves showing unique OTUs at the 95% threshold (a box graph at the rarefied sequence number), comparison of (B) Chao1 estimators and (C) phylogenetic diversity (PD) index between neurotypical (blue-colored box) and autistic (red-colored box) groups at different similarity thresholds (*: P<0.05, **: P<0.01 by one-tailed Mann-Whitney test).</p