Identification of toxoplasma gondii genes involved in the strain-specific modulation of IL-12 cytokine secretion

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Biology, 2011.Cataloged from PDF version of thesis.Includes bibliographical references (p. 15-18).Toxoplasma gondii is an intracellular pathogen that causes life-threatening toxoplasmosis in developing fetuses and immune-compromised individuals. An immune response to a Toxoplasma infection is characterized by the stimulation of high levels of interleukin- 12 (IL- 12), followed by the production of interferon-y (IFN-y) by immune cells. Although IFN-y is the main mediator of resistance to Toxoplasma infection, Toxoplasma is able to manipulate the immune response through the regulation of IL- 12 production. Toxoplasma has been shown to modulate the induction of IL-12 in a strain-specific manner. An infection with a type II strain, but not type I and III strains, induce high levels of IL- 12 production by macrophages in vitro. Previous studies have implicated two Toxoplasma genes that play a role in this strainspecific difference in IL- 12 production. A rhoptry protein kinase, ROP 16, from type I and III strains, was found to be crucial in the suppression of IL-12. A type II dense granule protein, GRA 15, induces IL-12 through NF-KB activation. I have screened F1 progeny from a type III x type II cross for IL-12 induction and NF-KB activation. My preliminary experiments indicate that there are other Toxoplasma factors involved in the strain-specific inhibition of IL-12, and this inhibition has a genetic basis. To understand the role of IL-12 regulation by Toxoplasma, I intend to (i) identify novel Toxoplasma genes involved in the inhibition of IL- 12 secretion (ii) test and characterize the effects of the Toxoplasma protein ROP38 on IL-12 signaling and (iii) determine the target/s of Toxoplasma inhibition of IL-12 production.by Renee Abba Mc Kell.S.M

Growth arrest-specific gene 6 expression in human breast cancer

Growth arrest-specific gene 6 (Gas6), identified in 1995, acts as the ligand to the Axl/Tyro3 family of tyrosine kinase receptors and exerts mitogenic activity when bound to these receptors. Overexpression of the Axl/Tyro3 receptor family has been found in breast, ovarian and lung tumours. Gas6 is upregulated 23-fold by progesterone acting through the progesterone receptor B (PRB). Recently, Gas6 has been shown to be a target for overexpression and amplification in breast cancer. Quantitative real-time PCR analysis was used to determine the levels of Gas6 mRNA expression in 49 primary breast carcinomas. Expression of PRB protein was evaluated immunohistochemically with a commercially available PRB antibody. The results showed a positive association between PRB protein and Gas6 mRNA levels (P=0.04). Gas6 correlated positively with a number of favourable prognostic variables including lymph node negativity (P=0.0002), younger age at diagnosis (P=0.04), smaller size of tumours (P=0.02), low Nottingham prognostic index scores (P=0.03) and low nuclear morphology (P=0.03). This study verifies for the first time the association between PRB and Gas6 in breast cancer tissue

Evaluation of clustering algorithms for gene expression data

BACKGROUND: Cluster analysis is an integral part of high dimensional data analysis. In the context of large scale gene expression data, a filtered set of genes are grouped together according to their expression profiles using one of numerous clustering algorithms that exist in the statistics and machine learning literature. A closely related problem is that of selecting a clustering algorithm that is "optimal" in some sense from a rather impressive list of clustering algorithms that currently exist. RESULTS: In this paper, we propose two validation measures each with two parts: one measuring the statistical consistency (stability) of the clusters produced and the other representing their biological functional congruence. Smaller values of these indices indicate better performance for a clustering algorithm. We illustrate this approach using two case studies with publicly available gene expression data sets: one involving a SAGE data of breast cancer patients and the other involving a time course cDNA microarray data on yeast. Six well known clustering algorithms UPGMA, K-Means, Diana, Fanny, Model-Based and SOM were evaluated. CONCLUSION: No single clustering algorithm may be best suited for clustering genes into functional groups via expression profiles for all data sets. The validation measures introduced in this paper can aid in the selection of an optimal algorithm, for a given data set, from a collection of available clustering algorithms

Unsupervised extremely randomized trees

International audienceIn this paper we present a method to compute dissimilarities on unlabeled data, based on extremely randomized trees. This method, Unsupervised Extremely Randomized Trees, is used jointly with a novel randomized labeling scheme we describe here, and that we call AddCl3. Unlike existing methods such as AddCl1 and AddCl2, no synthetic instances are generated, thus avoiding an increase in the size of the dataset. The empirical study of this method shows that Unsupervised Extremely Randomized Trees with AddCl3 provides competitive results regarding the quality of resulting clusterings, while clearly outperforming previous similar methods in terms of running time

Discovery and Preclinical Validation of Salivary Transcriptomic and Proteomic Biomarkers for the Non-Invasive Detection of Breast Cancer

A sensitive assay to identify biomarkers using non-invasively collected clinical specimens is ideal for breast cancer detection. While there are other studies showing disease biomarkers in saliva for breast cancer, our study tests the hypothesis that there are breast cancer discriminatory biomarkers in saliva using de novo discovery and validation approaches. This is the first study of this kind and no other study has engaged a de novo biomarker discovery approach in saliva for breast cancer detection. In this study, a case-control discovery and independent preclinical validations were conducted to evaluate the performance and translational utilities of salivary transcriptomic and proteomic biomarkers for breast cancer detection.Salivary transcriptomes and proteomes of 10 breast cancer patients and 10 matched controls were profiled using Affymetrix HG-U133-Plus-2.0 Array and two-dimensional difference gel electrophoresis (2D-DIGE), respectively. Preclinical validations were performed to evaluate the discovered biomarkers in an independent sample cohort of 30 breast cancer patients and 63 controls using RT-qPCR (transcriptomic biomarkers) and quantitative protein immunoblot (proteomic biomarkers). Transcriptomic and proteomic profiling revealed significant variations in salivary molecular biomarkers between breast cancer patients and matched controls. Eight mRNA biomarkers and one protein biomarker, which were not affected by the confounding factors, were pre-validated, yielding an accuracy of 92% (83% sensitive, 97% specific) on the preclinical validation sample set.Our findings support that transcriptomic and proteomic signatures in saliva can serve as biomarkers for the non-invasive detection of breast cancer. The salivary biomarkers possess discriminatory power for the detection of breast cancer, with high specificity and sensitivity, which paves the way for prediction model validation study followed by pivotal clinical validation

Methods for evaluating clustering algorithms for gene expression data using a reference set of functional classes

BACKGROUND: A cluster analysis is the most commonly performed procedure (often regarded as a first step) on a set of gene expression profiles. In most cases, a post hoc analysis is done to see if the genes in the same clusters can be functionally correlated. While past successes of such analyses have often been reported in a number of microarray studies (most of which used the standard hierarchical clustering, UPGMA, with one minus the Pearson's correlation coefficient as a measure of dissimilarity), often times such groupings could be misleading. More importantly, a systematic evaluation of the entire set of clusters produced by such unsupervised procedures is necessary since they also contain genes that are seemingly unrelated or may have more than one common function. Here we quantify the performance of a given unsupervised clustering algorithm applied to a given microarray study in terms of its ability to produce biologically meaningful clusters using a reference set of functional classes. Such a reference set may come from prior biological knowledge specific to a microarray study or may be formed using the growing databases of gene ontologies (GO) for the annotated genes of the relevant species. RESULTS: In this paper, we introduce two performance measures for evaluating the results of a clustering algorithm in its ability to produce biologically meaningful clusters. The first measure is a biological homogeneity index (BHI). As the name suggests, it is a measure of how biologically homogeneous the clusters are. This can be used to quantify the performance of a given clustering algorithm such as UPGMA in grouping genes for a particular data set and also for comparing the performance of a number of competing clustering algorithms applied to the same data set. The second performance measure is called a biological stability index (BSI). For a given clustering algorithm and an expression data set, it measures the consistency of the clustering algorithm's ability to produce biologically meaningful clusters when applied repeatedly to similar data sets. A good clustering algorithm should have high BHI and moderate to high BSI. We evaluated the performance of ten well known clustering algorithms on two gene expression data sets and identified the optimal algorithm in each case. The first data set deals with SAGE profiles of differentially expressed tags between normal and ductal carcinoma in situ samples of breast cancer patients. The second data set contains the expression profiles over time of positively expressed genes (ORF's) during sporulation of budding yeast. Two separate choices of the functional classes were used for this data set and the results were compared for consistency. CONCLUSION: Functional information of annotated genes available from various GO databases mined using ontology tools can be used to systematically judge the results of an unsupervised clustering algorithm as applied to a gene expression data set in clustering genes. This information could be used to select the right algorithm from a class of clustering algorithms for the given data set

High-throughput genomic technology in research and clinical management of breast cancer. Molecular signatures of progression from benign epithelium to metastatic breast cancer

It is generally accepted that early detection of breast cancer has great impact on patient survival, emphasizing the importance of early diagnosis. In a widely recognized model of breast cancer development, tumor cells progress through chronological and well defined stages. However, the molecular basis of disease progression in breast cancer remains poorly understood. High-throughput molecular profiling techniques are excellent tools for the study of complex molecular alterations. By accurately mapping changes in the genome and subsequent biological/molecular pathways, the chances of finding potential novel treatment targets as well as intervention strategies are enhanced, and ultimately lives can be saved. This review provides a brief summary of recent progress in identifying molecular markers for invasiveness in early breast lesions

The crystal structure of human Rogdi provides insight into the causes of Kohlschutter-Tonz Syndrome

Kohlschutter-Tönz syndrome (KTS) is a rare autosomal-recessive disorder of childhood onset characterized by global developmental delay, spasticity, epilepsy, and amelogenesis imperfecta. Rogdi, an essential protein, is highly conserved across metazoans, and mutations in Rogdi are linked to KTS. However, how certain mutations in Rogdi abolish its physiological functions and cause KTS is not known. In this study, we determined the crystal structure of human Rogdi protein at atomic resolution. Rogdi forms a novel elongated curved structure comprising the ?? domain, a leucine-zipper-like four-helix bundle, and a characteristic ??-sheet domain. Within the ?? domain, the N-terminal H1 helix (residues 19-45) pairs with the C-terminal H6 helix (residues 252-287) in an antiparallel manner, indicating that the integrity of the four-helix bundle requires both N- and C-terminal residues. The crystal structure, in conjunction with biochemical data, indicates that the ?? domain might undergo a conformational change and provide a structural platform for protein-protein interactions. Disruption of the four-helix bundle by mutation results in significant destabilization of the structure. This study provides structural insights into how certain mutations in Rogdi affect its structure and cause KTS, which has important implications for the development of pharmaceutical agents against this debilitating neurological disease

Clustering-based approaches to SAGE data mining

Serial analysis of gene expression (SAGE) is one of the most powerful tools for global gene expression profiling. It has led to several biological discoveries and biomedical applications, such as the prediction of new gene functions and the identification of biomarkers in human cancer research. Clustering techniques have become fundamental approaches in these applications. This paper reviews relevant clustering techniques specifically designed for this type of data. It places an emphasis on current limitations and opportunities in this area for supporting biologically-meaningful data mining and visualisation