PhyloScan: identification of transcription factor binding sites using cross-species evidence

BACKGROUND: When transcription factor binding sites are known for a particular transcription factor, it is possible to construct a motif model that can be used to scan sequences for additional sites. However, few statistically significant sites are revealed when a transcription factor binding site motif model is used to scan a genome-scale database. METHODS: We have developed a scanning algorithm, PhyloScan, which combines evidence from matching sites found in orthologous data from several related species with evidence from multiple sites within an intergenic region, to better detect regulons. The orthologous sequence data may be multiply aligned, unaligned, or a combination of aligned and unaligned. In aligned data, PhyloScan statistically accounts for the phylogenetic dependence of the species contributing data to the alignment and, in unaligned data, the evidence for sites is combined assuming phylogenetic independence of the species. The statistical significance of the gene predictions is calculated directly, without employing training sets. RESULTS: In a test of our methodology on synthetic data modeled on seven Enterobacteriales, four Vibrionales, and three Pasteurellales species, PhyloScan produces better sensitivity and specificity than MONKEY, an advanced scanning approach that also searches a genome for transcription factor binding sites using phylogenetic information. The application of the algorithm to real sequence data from seven Enterobacteriales species identifies novel Crp and PurR transcription factor binding sites, thus providing several new potential sites for these transcription factors. These sites enable targeted experimental validation and thus further delineation of the Crp and PurR regulons in E. coli. CONCLUSION: Better sensitivity and specificity can be achieved through a combination of (1) using mixed alignable and non-alignable sequence data and (2) combining evidence from multiple sites within an intergenic region

FOXD3 Regulates VISTA Expression in Melanoma.

Immune checkpoint inhibitors have improved patient survival in melanoma, but the innate resistance of many patients necessitates the investigation of alternative immune targets. Many immune checkpoint proteins lack proper characterization, including V-domain Ig suppressor of T cell activation (VISTA). VISTA expression on immune cells can suppress T cell activity; however, few studies have investigated its expression and regulation in cancer cells. In this study, we observe that VISTA is expressed in melanoma patient samples and cell lines. Tumor cell-specific expression of VISTA promotes tumor onset in vivo, associated with increased intratumoral T regulatory cells, and enhanced PDL-1 expression on tumor-infiltrating macrophages. VISTA transcript levels are regulated by the stemness factor Forkhead box D3 (FOXD3). BRAF inhibition upregulates FOXD3 and reduces VISTA expression. Overall, this study demonstrates melanoma cell expression of VISTA and its regulation by FOXD3, contributing to the rationale for therapeutic strategies that combine targeted inhibitors with immune checkpoint blockade

Pathologic Correlation of PET-CT Based Auto Contouring for Radiation Planning in Lung Cancer

Purpose/Objective(s): Radiation therapy in lung cancer relies on CT and functional imaging (FDG-PET) to delineate tumor volumes. Semi-automatic contouring tools have been developed for PET to improve on the inter-observer bias of manual contouring and intrinsic differences in imaging equipment. A common method involves using a threshold at a given percentage of the max activity, which may be less accurate with smaller tumors and tumors with low source to background ratio. To overcome this deficiency, a gradient algorithm, which detects changes in image counts at the border of the tumor, has been developed. Few studies have correlated these methods to pathological specimens. American Society for Therapeutic Radiation Oncology (ASTRO) 52nd Annual Meeting October 31 - November 4, San Diego, C

Identification and validation of genetic variants predictive of gait in standardbred horses

Several horse breeds have been specifically selected for the ability to exhibit alternative patterns of locomotion, or gaits. A premature stop codon in the gene DMRT3 is permissive for “gaitedness” across breeds. However, this mutation is nearly fixed in both American Standardbred trotters and pacers, which perform a diagonal and lateral gait, respectively, during harness racing. This suggests that modifying alleles must influence the preferred gait at racing speeds in these populations. A genome-wide association analysis for the ability to pace was performed in 542 Standardbred horses (n = 176 pacers, n = 366 trotters) with genotype data imputed to ~74,000 single nucleotide polymorphisms (SNPs). Nineteen SNPs on nine chromosomes (ECA1, 2, 6, 9, 17, 19, 23, 25, 31) reached genome-wide significance (p < 1.44 x 10−6). Variant discovery in regions of interest was carried out via whole-genome sequencing. A set of 303 variants from 22 chromosomes with putative modifying effects on gait was genotyped in 659 Standardbreds (n = 231 pacers, n = 428 trotters) using a high-throughput assay. Random forest classification analysis resulted in an out-of-box error rate of 0.61%. A conditional inference tree algorithm containing seven SNPs predicted status as a pacer or trotter with 99.1% accuracy and subsequently performed with 99.4% accuracy in an independently sampled population of 166 Standardbreds (n = 83 pacers, n = 83 trotters). This highly accurate algorithm could be used by owners/trainers to identify Standardbred horses with the potential to race as pacers or as trotters, according to the genotype identified, prior to initiating training and would enable fine-tuning of breeding programs with designed matings. Additional work is needed to determine both the algorithm’s utility in other gaited breeds and whether any of the predictive SNPs play a physiologically functional role in the tendency to pace or tag true functional alleles

Framework Report: The AIDS Accountability Workplace Scorecard, September 2011

The aim of the AIDS Accountability Workplace Scorecard is to improve HIV and AIDS workplace programmes in the countries and sectors most affected by the disease, and improve the health of employees, their families and communities. Through this initiative we will: / 1. Provide tools for HIV and AIDS workplace programme monitoring and evaluation AAI has developed scorecard tools for small, medium and large workplaces, which can be used to assess a global, regional or national HIV and AIDS programme or interventions at a specific workplace site. The scorecards can serve as both internal monitoring and evaluation tools and as assessments to present to stakeholders within and outside the organization. / 2. Publish annual Rankings of HIV and AIDS Workplace Programmes Scorecard users who wish to receive a ranking analysis and recommendations for how to improve their programmes can submit their scorecards to AAI. AAI ‘s ranking analysis will allow users to compare their performance with others and over time also measure their own progress. Respondents will be encouraged to publish their ranking in AAI’s yearly Ranking Reports. / 3. Share good practice The knowledge and good practices generated through the published rankings will be used to stimulate improved HIV and AIDS Workplace Programmes worldwide. Large networks of companies, trade union confederations, and national and international organizations can use the scorecard as a common framework for monitoring and evaluation of workplace programmes

Smooth muscle cells affect differential nanoparticle accumulation in disturbed blood flow-induced murine atherosclerosis

Atherosclerosis is a lipid-driven chronic inflammatory disease that leads to the formation of plaques in the inner lining of arteries. Plaques form over a range of phenotypes, the most severe of which is vulnerable to rupture and causes most of the clinically significant events. In this study, we evaluated the efficacy of nanoparticles (NPs) to differentiate between two plaque phenotypes based on accumulation kinetics in a mouse model of atherosclerosis. This model uses a perivascular cuff to induce two regions of disturbed wall shear stress (WSS) on the inner lining of the instrumented artery, low (upstream) and multidirectional (downstream), which, in turn, cause the development of an unstable and stable plaque phenotype, respectively. To evaluate the influence of each WSS condition, in addition to the final plaque phenotype, in determining NP uptake, mice were injected with NPs at intermediate and fully developed stages of plaque growth. The kinetics of artery wall uptake were assessed in vivo using dynamic contrast-enhanced magnetic resonance imaging. At the intermediate stage, there was no difference in NP uptake between the two WSS conditions, although both were different from the control arteries. At the fully-developed stage, however, NP uptake was reduced in plaques induced by low WSS, but not multidirectional WSS. Histological evaluation of plaques induced by low WSS revealed a significant inverse correlation between the presence of smooth muscle cells and NP accumulation, particularly at the plaque-lumen interface, which did not exist with other constituents (lipid and collagen) and was not present in plaques induced by multidirectional WSS. These findings demonstrate that NP accumulation can be used to differentiate between unstable and stable murine atherosclerosis, but accumulation kinetics are not directly influenced by the WSS condition. This tool could be used as a diagnostic to evaluate the efficacy of experimental therapeutics for atherosclerosis

A ratiometric-based measure of gene co-expression

Background: Gene co-expression analysis has previously been based on measures that include correlation coefficients and mutual information, as well as newcomers such as MIC. These measures depend primarily on the degree of association between the RNA levels of two genes and to a lesser extent on their variability. They focus on the similarity of expression value trajectories that change in like manner across samples. However there are relationships of biological interest for which these classical measures are expected to be insensitive. These include genes whose expression levels are ratiometrically stable and genes whose variance is tightly constrained. Large-scale studies of relatively homogeneous samples, including single cell RNA-seq, are experimental settings in which such relationships might be especially pertinent. Results: We develop and implement a ratiometric approach for detecting gene associations (abbreviated RA). It is based on the coefficient of variation of the measured expression ratio of each pair of genes. We apply it to a collection of lymphoblastoid RNA-seq data from the 1000 Genomes Project Consortium, a typical sample set with high overall homogeneity. RA is a selective method, reporting in this case ~1/4 of all possible gene pairs, yet these relationships include a distilled picture of biological relationships previously found by other methods. In addition, RA reveals expression relationships that are not detected by traditional correlation and mutual information methods. We also analyze data from individual lymphoblastoid cells and show that desirable properties of the RA method extend to single-cell RNA-seq. Conclusion: We show that our ratiometric method identifies biologically significant relationships that are often missed or low-ranked by conventional association-based methods when applied to a relatively homogenous dataset. The results open new questions about the regulatory mechanisms that produce strong RA relationships. RA is scalable and potentially well suited for the analysis of thousands of bulk-RNA or single-cell transcriptomes