Prefoldin 6 mediates longevity response from heat shock factor 1 to FOXO in C-elegans

Heat shock factor 1 (HSF-1) and forkhead box O (FOXO) are key transcription factors that protect cells from various stresses. In Caenorhabditis elegans, HSF-1 and FOXO together promote a long life span when insulin/IGF-1 signaling (IIS) is reduced. However, it remains poorly understood how HSF-1 and FOXO cooperate to confer IIS-mediated longevity. Here, we show that prefoldin 6 (PFD-6), a component of the molecular chaperone prefoldin-like complex, relays longevity response from HSF-1 to FOXO under reduced IIS. We found that PFD-6 was specifically required for reduced IIS-mediated longevity by acting in the intestine and hypodermis. We showed that HSF-1 increased the levels of PFD-6 proteins, which in turn directly bound FOXO and enhanced its transcriptional activity. Our work suggests that the prefoldin-like chaperone complex mediates longevity response from HSF-1 to FOXO to increase the life span in animals with reduced IIS.11Ysciescopu

Liver and Adipose Expression Associated SNPs Are Enriched for Association to Type 2 Diabetes

Genome-wide association studies (GWAS) have demonstrated the ability to identify the strongest causal common variants in complex human diseases. However, to date, the massive data generated from GWAS have not been maximally explored to identify true associations that fail to meet the stringent level of association required to achieve genome-wide significance. Genetics of gene expression (GGE) studies have shown promise towards identifying DNA variations associated with disease and providing a path to functionally characterize findings from GWAS. Here, we present the first empiric study to systematically characterize the set of single nucleotide polymorphisms associated with expression (eSNPs) in liver, subcutaneous fat, and omental fat tissues, demonstrating these eSNPs are significantly more enriched for SNPs that associate with type 2 diabetes (T2D) in three large-scale GWAS than a matched set of randomly selected SNPs. This enrichment for T2D association increases as we restrict to eSNPs that correspond to genes comprising gene networks constructed from adipose gene expression data isolated from a mouse population segregating a T2D phenotype. Finally, by restricting to eSNPs corresponding to genes comprising an adipose subnetwork strongly predicted as causal for T2D, we dramatically increased the enrichment for SNPs associated with T2D and were able to identify a functionally related set of diabetes susceptibility genes. We identified and validated malic enzyme 1 (Me1) as a key regulator of this T2D subnetwork in mouse and provided support for the association of this gene to T2D in humans. This integration of eSNPs and networks provides a novel approach to identify disease susceptibility networks rather than the single SNPs or genes traditionally identified through GWAS, thereby extracting additional value from the wealth of data currently being generated by GWAS

Probabilistic Inference for Nucleosome Positioning with MNase-Based or Sonicated Short-Read Data

We describe a model-based method, PING, for predicting nucleosome positions in MNase-Seq and MNase- or sonicated-ChIP-Seq data. PING compares favorably to NPS and TemplateFilter in scalability, accuracy and robustness to low read density. To demonstrate that PING predictions from widely available sonicated data can have sufficient spatial resolution to be to be useful for biological inference, we use Illumina H3K4me1 ChIP-seq data to detect changes in nucleosome positioning around transcription factor binding sites due to tamoxifen stimulation, to discriminate functional and non-functional transcription factor binding sites more effectively than with enrichment profiles, and to confirm that the pioneer transcription factor Foxa2 associates with the accessible major groove of nucleosomal DNA

AKR1C1 as a Biomarker for Differentiating the Biological Effects of Combustible from Non-Combustible Tobacco Products

Smoking has been established as a major risk factor for developing oral squamous cell carcinoma (OSCC), but less attention has been paid to the effects of smokeless tobacco products. Our objective is to identify potential biomarkers to distinguish the biological effects of combustible tobacco products from those of non-combustible ones using oral cell lines. Normal human gingival epithelial cells (HGEC), non-metastatic (101A) and metastatic (101B) OSCC cell lines were exposed to different tobacco product preparations (TPPs) including cigarette smoke total particulate matter (TPM), whole-smoke conditioned media (WS-CM), smokeless tobacco extract in complete artificial saliva (STE), or nicotine (NIC) alone. We performed microarray-based gene expression profiling and found 3456 probe sets from 101A, 1432 probe sets from 101B, and 2717 probe sets from HGEC to be differentially expressed. Gene Set Enrichment Analysis (GSEA) revealed xenobiotic metabolism and steroid biosynthesis were the top two pathways that were upregulated by combustible but not by non-combustible TPPs. Notably, aldo-keto reductase genes, AKR1C1 and AKR1C2, were the core genes in the top enriched pathways and were statistically upregulated more than eight-fold by combustible TPPs. Quantitative real time polymerase chain reaction (qRT-PCR) results statistically support AKR1C1 as a potential biomarker for differentiating the biological effects of combustible from non-combustible tobacco products

Model-based nucleosome occupancy profiles for sonicated H3K4me1 ChIP-Seq data.

<p>Panels show nucleosome positioning within bp from the top-ranked <i>in vivo</i> transcription factor binding sites that PICS detected for (A) SPI1 and (B) CEBPB from sonicated H3K4me1 ChIP-Seq data for 0 hour (blue) and 1 hour (red) after tamoxifen stimulation <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0032095#pone.0032095-Heinz1" target="_blank">[20]</a>. The heatmaps show nucleosome prediction profiles for each region as pairs of blue/red horizontal lines, with darker colors indicating higher scoring, i.e. better positioned, nucleosomes. The lower part of each heatmap shows genomic regions that lack detectable nucleosome positioning. Curves below each heatmap show average occupancy profiles across all TF regions.</p

Modality and nucleosome occupancy for Foxa2 and Pdx1 binding sites in mouse adult islet tissue.

<p>Panels show the modality and nucleosome profiles for <i>in vivo</i> binding sites of the transcription factors Foxa2 (left) and Pdx1 (right) <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0032095#pone.0032095-Hoffman1" target="_blank">[19]</a>. (A) The number of binding sites in bimodal(bi), monomodal(mono) and NoNuc(No) groups. A NoNuc transcription factor binding site had no H3K4me1-marked nucleosome within 1 kb of its peak summit, a monomodal site had at least one H3K4me1 nucleosome within 50 bp of its summit, and all other sites were bimodal. (B) Average model-based nucleosome positioning profiles for the three classes of binding sites.</p

Expression levels for genes associated with different types of nucleosome predictions.

<p>RNA-seq data for mouse adult islets are from <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0032095#pone.0032095-Kim1" target="_blank">[29]</a>. Nucleosomes were predicted from H3K4me1 data for (A,B) mouse adult islets and (C,D) mouse adult liver <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0032095#pone.0032095-Hoffman1" target="_blank">[19]</a>. Dashed horizontal lines show medians. In islets, genes categorized as bimodal and NoNuc respectively have significantly higher and lower expression levels than those in the monomodal group. Nucleosome prediction groups are outlined in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0032095#pone-0032095-g003" target="_blank">Fig. 3</a>'s caption and in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0032095#s4" target="_blank">Methods</a>.</p

Truncated AUC statistics for PING, TpF and NPS.

<p>Panels show the area under ROC curves (AUC), truncated at a specificity of 0.8, as a function of number of reads in random subsets for PING, TpF and NPS. A larger AUC value corresponds to a more accurate method; the maximum possible AUC value for the truncated curves is 0.2. Datasets are (A) MNase-Seq data from budding yeast <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0032095#pone.0032095-Kaplan1" target="_blank">[21]</a>, (B) sonicated H3K4me1 ChIP-Seq data from a mouse cell line <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0032095#pone.0032095-Heinz1" target="_blank">[20]</a>, and (C) sonicated H3K4me1 ChIP-Seq data from mouse adult islet tissue <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0032095#pone.0032095-Hoffman1" target="_blank">[19]</a>.</p