Upper Bound on the Decay τ→μγ from the Belle Detector

journal articl

CERTAIN DOUBLE WHITTAKER TRANSFORMS OF GENERALIZED HYPERGEOMETRIC FUNCTIONS

application/pdfdepartmental bulletin pape

Japanese Newspapers : Expression Differences in Japanese and English (Video)

conference objec

ATP6V0C is an essential host factor for HCMV replication.

<p>(A) Human Fibroblast cells were transfected with siRNAs (40 nM) against each of the six HCMV miRNA targets previously identified. Cells were then infected with a GFP tagged HCMV virus at an MOI of one, 16 hours post transfection. GFP levels were monitored during the course of infection and compared to determine the effect of knock down of the miRNA targets on HCMV replication. Data represents 4 biological repeats. (B) Fibroblast cells were transfected with three independent siRNAs targeting ATP6V0C (stl1, 2 and 3) as above. Each siRNA resulted in reduced virus growth as determined by GFP fluorescence. Data represents 4 biological repeats. (C) Cells were transfected with pooled siRNAs and infected as above with cells and supernatant harvested at the indicated time points and analysed for infectious virus by plaque assay. Data represents 3 biological repeats (D) Fibroblast cells were transfected with siRNAs as above targeting ATP6V1A or ATP6V1H, components of the V-ATPase complex and HCMV replication analysed by GFP fluorescence. Replication was inhibited by similar levels as observed for knockdown of ATP6V0C. Data represents 4 biological repeats.</p

Deletion of US25-1 from HCMV results in loss of enrichment of identified targets.

<p>Enrichment of the six US25-1 targets was determined by RT-PCR following RISC-IP from cells infected with wild type virus or cells infected with US25 knock out viruses. Enrichment levels converted to percentage with enrichment from AD169 infected cells set at 100%. Raw enrichment values shown above each bar. Expression levels were corrected against GAPDH and each bar represents 3 biological repeats. * = statistically significant difference (p = <0.01). Statistics were performed on raw data using the Mann-Whitney non-parametric U-test.</p

Data_Sheet_1_Natural Product Target Network Reveals Potential for Cancer Combination Therapies.docx

A body of research demonstrates examples of in vitro and in vivo synergy between natural products and anti-neoplastic drugs for some cancers. However, the underlying biological mechanisms are still elusive. To better understand biological entities targeted by natural products and therefore provide rational evidence for future novel combination therapies for cancer treatment, we assess the targetable space of natural products using public domain compound-target information. When considering pathways from the Reactome database targeted by natural products, we found an increase in coverage of 61% (725 pathways), relative to pathways covered by FDA approved cancer drugs collected in the Cancer Targetome, a resource for evidence-based drug-target interactions. Not only is the coverage of pathways targeted by compounds increased when we include natural products, but coverage of targets within those pathways is also increased. Furthermore, we examined the distribution of cancer driver genes across pathways to assess relevance of natural products to critical cancer therapeutic space. We found 24 pathways enriched for cancer drivers that had no available cancer drug interactions at a potentially clinically relevant binding affinity threshold of < 100nM that had at least one natural product interaction at that same binding threshold. Assessment of network context highlighted the fact that natural products show target family groupings both distinct from and in common with cancer drugs, strengthening the complementary potential for natural products in the cancer therapeutic space. In conclusion, our study provides a foundation for developing novel cancer treatment with the combination of drugs and natural products.</p

Top 30 enriched genes contain multiple target sites for HCMV miRNAs.

<p>Sequences for the top 30 enriched transcripts were analysed for HCMV miRNA targets using RNA Hybrid algorithm. Figure shows hit matrix where a yellow square indicates at least one target site for the indicated HCMV miRNA. Independent hit matrices shown for targets either within the whole transcript, or within the CDS, 5′UTR and 3′UTR. Total number of potential miRNAs targeting a transcript shown in the far left column.</p

US25-1 targeting of ATP6V0C occurs through the predicted target site within the ORF.

<p>(A) The predicted US25-1 target site from ATP6V0C was cloned downstream of the luciferase reporter construct psiCheck2. The schematic shows the cloning strategy for ATP6V0C. (B) Schematic representation of sequence changes within the target site of ATP6V0C and the corresponding mutation in US25-1 mimic (sequence alterations in the US25-1 mimic are shown in red). (C) Constructs were co-transfected into HEK293 cells with US25-1 mimic, negative control siRNA or a US25-1 mimic with a mutated seed sequence at 40 nM. Data represents 3 biological replicates with standard deviation.</p

Data_Sheet_1_Network-Based Predictors of Progression in Head and Neck Squamous Cell Carcinoma.zip

<p>The heterogeneity in head and neck squamous cell carcinoma (HNSCC) has made reliable stratification extremely challenging. Behavioral risk factors such as smoking and alcohol consumption contribute to this heterogeneity. To help elucidate potential mechanisms of progression in HNSCC, we focused on elucidating patterns of gene interactions associated with tumor progression. We performed de-novo gene co-expression network inference utilizing 229 patient samples from The Cancer Genome Atlas (TCGA) previously annotated by Bornstein et al. (2016). Differential network analysis allowed us to contrast progressor and non-progressor cohorts. Beyond standard differential expression (DE) analysis, this approach evaluates changes in gene expression variance (differential variability DV) and changes in covariance, which we denote as differential wiring (DW). The set of affected genes was overlaid onto the co-expression network, identifying 12 modules significantly enriched in DE, DV, and/or DW genes. Additionally, we identified modules correlated with behavioral measures such as alcohol consumption and smoking status. In the module enriched for differentially wired genes, we identified network hubs including IL10RA, DOK2, APBB1IP, UBASH3A, SASH3, CELF2, TRAF3IP3, GIMAP6, MYO1F, NCKAP1L, WAS, FERMT3, SLA, SELPLG, TNFRSF1B, WIPF1, AMICA1, PTPN22; the network centrality and progression specificity of these genes suggest a potential role in tumor evolution mechanisms related to inflammation and microenvironment. The identification of this network-based gene signature could be further developed to guide progression stratification, highlighting how network approaches may help improve clinical research end points and ultimately aid in clinical utility.</p

Presentation_1_Network-Based Predictors of Progression in Head and Neck Squamous Cell Carcinoma.pdf

<p>The heterogeneity in head and neck squamous cell carcinoma (HNSCC) has made reliable stratification extremely challenging. Behavioral risk factors such as smoking and alcohol consumption contribute to this heterogeneity. To help elucidate potential mechanisms of progression in HNSCC, we focused on elucidating patterns of gene interactions associated with tumor progression. We performed de-novo gene co-expression network inference utilizing 229 patient samples from The Cancer Genome Atlas (TCGA) previously annotated by Bornstein et al. (2016). Differential network analysis allowed us to contrast progressor and non-progressor cohorts. Beyond standard differential expression (DE) analysis, this approach evaluates changes in gene expression variance (differential variability DV) and changes in covariance, which we denote as differential wiring (DW). The set of affected genes was overlaid onto the co-expression network, identifying 12 modules significantly enriched in DE, DV, and/or DW genes. Additionally, we identified modules correlated with behavioral measures such as alcohol consumption and smoking status. In the module enriched for differentially wired genes, we identified network hubs including IL10RA, DOK2, APBB1IP, UBASH3A, SASH3, CELF2, TRAF3IP3, GIMAP6, MYO1F, NCKAP1L, WAS, FERMT3, SLA, SELPLG, TNFRSF1B, WIPF1, AMICA1, PTPN22; the network centrality and progression specificity of these genes suggest a potential role in tumor evolution mechanisms related to inflammation and microenvironment. The identification of this network-based gene signature could be further developed to guide progression stratification, highlighting how network approaches may help improve clinical research end points and ultimately aid in clinical utility.</p