Women and Stability: A Topological View of the Relationship between Women and Armed Conflict in West Africa

The relationship between women and stability, if any, is a topic of much debate and research. Several large and influential organizations have all researched women\u27s effect on stability. Furthermore, several of these world organizations, the United Nations, in particular, have declared gender equality to be a driving force in promoting stability and conflict prevention. Due to the United States active involvement in conflict prevention in such regions as West Africa, research concerning the relationship between women and stability is of particular interest to the United States Africa Command. As such, this research applied Topological Data Analysis, combined with other machine learning algorithms, to Demographic and Health Survey Program data combined with Armed Conflict Location and Event Data so as to observe the relationship between women\u27s status and armed conflicts in the West African region. While this team did not observe any direct correlation between women\u27s well-being and stability - defined as a lack of armed conflict events - the chosen methodologies and data usage have potential implications for future research concerning stability and conflict

Development of a Screening Assay for Type III Secretion System Inhibitors and High Throughput Screening Campaign of Inhibitors of PRP of Staphylococcus aureus

Antibiotics inhibit the growth or survival of bacteria by targeting their essential functions.1 Due to weaknesses in traditional antibiotics and the increasing prevalence of antibiotic resistance genes, virulence factors are being targeted for therapeutic treatment of bacterial infection.2 We have developed an assay to quantify and observe type III secretion system (T3SS) activity. The type III secretion system (T3SS) is a virulence factor present in some Gram-negative pathogens including enteropathogenic and enterohemorrhagic E. coli (EPEC and EHEC, respectively),3 and others.4–9 The T3SS between EPEC and EHEC are highly conserved and share over 90% sequence identity with the mouse pathogen Citrobacter rodentium.3,10,11 Because of the high similarity between the pathophysiology of these organisms, C. rodentium is often used as the mouse model of EPEC and EHEC infection.12–14 We have developed a construct of C. rodentium to produce carboxypeptidase G2 (CPG2), a eukaryotic enzyme that selectively cleaves glutamate residues. We have tagged CPG2 on the N-terminus with an amino acid sequence to target the enzyme for type III secretion.15 The CPG2 reporter assay was used to screen natural products for their ability to inhibit the T3SS.16–26 We also completed a high throughput screening campaign for the identification of inhibitors of phage-related ribosomal protease Prp. This protease is essential for ribosomal assembly in bacteria and previously reported knockdown studies have indicated that bacteria cannot survive without Prp. We have screened over 5000 compounds for their inhibitory activity

O-Glycosylation of transcription factor BCL11b: truth or myth?

BCL11B, a gene regulatory transcription factor, is known to be a regulator of T cell development in the immune system, and has been identified as a tumor suppressor for T cell leukemias. Defining the molecular events that regulate the activity of BCL11B will provide potential drug targets that will aid those with T cell leukemia. In addition, the development of T cells will be better understood, providing more knowledge of the immune system. We know that BCL11B activity changes when developing T cells (known as thymocytes) are stimulated and that this activity change coincides with changes in the levels of phosphorylation and sumoylation of the factor. Additionally, we know that many nuclear proteins are modified by O-glycosylation, and that this modification is often mutually exclusive with phosphorylation. Working in Dr. Filtz’s lab this winter term, I obtained preliminary evidence that BCL11B is O-glycosylated. We investigated BCL11B for O-glycosylation and discovered that BCL11B is not glycosylated, but changing glycosylation levels in the system changes the phosphorylation of BCL11B

Genomic analyses with biofilter 2.0: knowledge driven filtering, annotation, and model development

BACKGROUND: The ever-growing wealth of biological information available through multiple comprehensive database repositories can be leveraged for advanced analysis of data. We have now extensively revised and updated the multi-purpose software tool Biofilter that allows researchers to annotate and/or filter data as well as generate gene-gene interaction models based on existing biological knowledge. Biofilter now has the Library of Knowledge Integration (LOKI), for accessing and integrating existing comprehensive database information, including more flexibility for how ambiguity of gene identifiers are handled. We have also updated the way importance scores for interaction models are generated. In addition, Biofilter 2.0 now works with a range of types and formats of data, including single nucleotide polymorphism (SNP) identifiers, rare variant identifiers, base pair positions, gene symbols, genetic regions, and copy number variant (CNV) location information. RESULTS: Biofilter provides a convenient single interface for accessing multiple publicly available human genetic data sources that have been compiled in the supporting database of LOKI. Information within LOKI includes genomic locations of SNPs and genes, as well as known relationships among genes and proteins such as interaction pairs, pathways and ontological categories. Via Biofilter 2.0 researchers can: • Annotate genomic location or region based data, such as results from association studies, or CNV analyses, with relevant biological knowledge for deeper interpretation • Filter genomic location or region based data on biological criteria, such as filtering a series SNPs to retain only SNPs present in specific genes within specific pathways of interest • Generate Predictive Models for gene-gene, SNP-SNP, or CNV-CNV interactions based on biological information, with priority for models to be tested based on biological relevance, thus narrowing the search space and reducing multiple hypothesis-testing. CONCLUSIONS: Biofilter is a software tool that provides a flexible way to use the ever-expanding expert biological knowledge that exists to direct filtering, annotation, and complex predictive model development for elucidating the etiology of complex phenotypic outcomes

Limited Systemic Sclerosis Patients with Pulmonary Arterial Hypertension Show Biomarkers of Inflammation and Vascular Injury

Pulmonary arterial hypertension (PAH) is a common complication for individuals with limited systemic sclerosis (lSSc). The identification and characterization of biomarkers for lSSc-PAH should lead to less invasive screening, a better understanding of pathogenesis, and improved treatment.Forty-nine PBMC samples were obtained from 21 lSSc subjects without PAH (lSSc-noPAH), 15 lSSc subjects with PAH (lSSc-PAH), and 10 healthy controls; three subjects provided PBMCs one year later. Genome-wide gene expression was measured for each sample. The levels of 89 cytokines were measured in serum from a subset of subjects by Multi-Analyte Profiling (MAP) immunoassays. Gene expression clearly distinguished lSSc samples from healthy controls, and separated lSSc-PAH from lSSc-NoPAH patients. Real-time quantitative PCR confirmed increased expression of 9 genes (ICAM1, IFNGR1, IL1B, IL13Ra1, JAK2, AIF1, CCR1, ALAS2, TIMP2) in lSSc-PAH patients. Increased circulating cytokine levels of inflammatory mediators such as TNF-alpha, IL1-beta, ICAM-1, and IL-6, and markers of vascular injury such as VCAM-1, VEGF, and von Willebrand Factor were found in lSSc-PAH subjects.The gene expression and cytokine profiles of lSSc-PAH patients suggest the presence of activated monocytes, and show markers of vascular injury and inflammation. These genes and factors could serve as biomarkers of PAH involvement in lSSc

Synthesis-View: visualization and interpretation of SNP association results for multi-cohort, multi-phenotype data and meta-analysis

<p>Abstract</p> <p>Background</p> <p>Initial genome-wide association study (GWAS) discoveries are being further explored through the use of large cohorts across multiple and diverse populations involving meta-analyses within large consortia and networks. Many of the additional studies characterize less than 100 single nucleotide polymorphisms (SNPs), often include multiple and correlated phenotypic measurements, and can include data from multiple-sites, multiple-studies, as well as multiple race/ethnicities. New approaches for visualizing resultant data are necessary in order to fully interpret results and obtain a broad view of the trends between DNA variation and phenotypes, as well as provide information on specific SNP and phenotype relationships.</p> <p>Results</p> <p>The Synthesis-View software tool was designed to visually synthesize the results of the aforementioned types of studies. Presented herein are multiple examples of the ways Synthesis-View can be used to report results from association studies of DNA variation and phenotypes, including the visual integration of p-values or other metrics of significance, allele frequencies, sample sizes, effect size, and direction of effect.</p> <p>Conclusions</p> <p>To truly allow a user to visually integrate multiple pieces of information typical of a genetic association study, innovative views are needed to integrate multiple pieces of information. As a result, we have created "Synthesis-View" software for the visualization of genotype-phenotype association data in multiple cohorts. Synthesis-View is freely available for non-commercial research institutions, for full details see <url>https://chgr.mc.vanderbilt.edu/synthesisview</url>.</p