Identification and characterisation of microRNAs expressed in articular cartilage and osteoarthritis

Osteoarthritis (OA) is a debilitating disease suffered by millions of people around the world. It affects the whole joint but is defined in part by a degradation of cartilage which becomes more pronounced as the disease progresses. The initiation and progression of OA is still not fully characterised. MicroRNAs are small, non-coding RNA molecules which are known to regulate gene expression, however, at the moment it is unclear exactly how many may be expressed in cartilage or how their expression may alter during osteoarthritis. Using human OA cartilage and cells derived from it, we have optimised small RNA purification and have shown that there is a clear difference between microRNA expression in cartilage compared with cell isolates, with miR-140 most highly expressed in cells immediately after digestion. This may suggest a stress-type response. RNA purified from chondrocytes immediately after digestion from cartilage was utilised for Illumina GIIX deep sequencing in an attempt to identify novel microRNAs in addition to expressed known microRNAs. 990 previously documented and 1621 potential novel microRNAs were discovered and 16 novel candidates were chosen for further validation. Validation was carried out using northern blotting and qRT-PCR on a number of samples and tissue types. In addition, the selected candidates were also examined in chondrogenesis models, and one candidate was probed in mouse embryo in situ hybridisation in an attempt to localise the candidate to cartilage tissue. Three of the candidates with the most promising validation results were analysed for function using a whole genome microarray combined with computational target search analysis. A number of likely targets obtained from the array were further validated by subcloning and luciferase assays, resulting in the discovery of ITGA5 as a target of candidate novel 11. The outcomes from this research provide us with an increased understanding of the miRNome of human OA cartilage and could impact upon future drug development and the use of microRNAs as biomarkers

Analysing functional genomics data using novel ensemble, consensus and data fusion techniques

Motivation: A rapid technological development in the biosciences and in computer science in the last decade has enabled the analysis of high-dimensional biological datasets on standard desktop computers. However, in spite of these technical advances, common properties of the new high-throughput experimental data, like small sample sizes in relation to the number of features, high noise levels and outliers, also pose novel challenges. Ensemble and consensus machine learning techniques and data integration methods can alleviate these issues, but often provide overly complex models which lack generalization capability and interpretability. The goal of this thesis was therefore to develop new approaches to combine algorithms and large-scale biological datasets, including novel approaches to integrate analysis types from different domains (e.g. statistics, topological network analysis, machine learning and text mining), to exploit their synergies in a manner that provides compact and interpretable models for inferring new biological knowledge. Main results: The main contributions of the doctoral project are new ensemble, consensus and cross-domain bioinformatics algorithms, and new analysis pipelines combining these techniques within a general framework. This framework is designed to enable the integrative analysis of both large- scale gene and protein expression data (including the tools ArrayMining, Top-scoring pathway pairs and RNAnalyze) and general gene and protein sets (including the tools TopoGSA , EnrichNet and PathExpand), by combining algorithms for different statistical learning tasks (feature selection, classification and clustering) in a modular fashion. Ensemble and consensus analysis techniques employed within the modules are redesigned such that the compactness and interpretability of the resulting models is optimized in addition to the predictive accuracy and robustness. The framework was applied to real-word biomedical problems, with a focus on cancer biology, providing the following main results: (1) The identification of a novel tumour marker gene in collaboration with the Nottingham Queens Medical Centre, facilitating the distinction between two clinically important breast cancer subtypes (framework tool: ArrayMining) (2) The prediction of novel candidate disease genes for Alzheimer’s disease and pancreatic cancer using an integrative analysis of cellular pathway definitions and protein interaction data (framework tool: PathExpand, collaboration with the Spanish National Cancer Centre) (3) The prioritization of associations between disease-related processes and other cellular pathways using a new rule-based classification method integrating gene expression data and pathway definitions (framework tool: Top-scoring pathway pairs) (4) The discovery of topological similarities between differentially expressed genes in cancers and cellular pathway definitions mapped to a molecular interaction network (framework tool: TopoGSA, collaboration with the Spanish National Cancer Centre) In summary, the framework combines the synergies of multiple cross-domain analysis techniques within a single easy-to-use software and has provided new biological insights in a wide variety of practical settings

Analysing functional genomics data using novel ensemble, consensus and data fusion techniques

Motivation: A rapid technological development in the biosciences and in computer science in the last decade has enabled the analysis of high-dimensional biological datasets on standard desktop computers. However, in spite of these technical advances, common properties of the new high-throughput experimental data, like small sample sizes in relation to the number of features, high noise levels and outliers, also pose novel challenges. Ensemble and consensus machine learning techniques and data integration methods can alleviate these issues, but often provide overly complex models which lack generalization capability and interpretability. The goal of this thesis was therefore to develop new approaches to combine algorithms and large-scale biological datasets, including novel approaches to integrate analysis types from different domains (e.g. statistics, topological network analysis, machine learning and text mining), to exploit their synergies in a manner that provides compact and interpretable models for inferring new biological knowledge. Main results: The main contributions of the doctoral project are new ensemble, consensus and cross-domain bioinformatics algorithms, and new analysis pipelines combining these techniques within a general framework. This framework is designed to enable the integrative analysis of both large- scale gene and protein expression data (including the tools ArrayMining, Top-scoring pathway pairs and RNAnalyze) and general gene and protein sets (including the tools TopoGSA , EnrichNet and PathExpand), by combining algorithms for different statistical learning tasks (feature selection, classification and clustering) in a modular fashion. Ensemble and consensus analysis techniques employed within the modules are redesigned such that the compactness and interpretability of the resulting models is optimized in addition to the predictive accuracy and robustness. The framework was applied to real-word biomedical problems, with a focus on cancer biology, providing the following main results: (1) The identification of a novel tumour marker gene in collaboration with the Nottingham Queens Medical Centre, facilitating the distinction between two clinically important breast cancer subtypes (framework tool: ArrayMining) (2) The prediction of novel candidate disease genes for Alzheimer’s disease and pancreatic cancer using an integrative analysis of cellular pathway definitions and protein interaction data (framework tool: PathExpand, collaboration with the Spanish National Cancer Centre) (3) The prioritization of associations between disease-related processes and other cellular pathways using a new rule-based classification method integrating gene expression data and pathway definitions (framework tool: Top-scoring pathway pairs) (4) The discovery of topological similarities between differentially expressed genes in cancers and cellular pathway definitions mapped to a molecular interaction network (framework tool: TopoGSA, collaboration with the Spanish National Cancer Centre) In summary, the framework combines the synergies of multiple cross-domain analysis techniques within a single easy-to-use software and has provided new biological insights in a wide variety of practical settings

Cancer Biomarker Discovery: The Entropic Hallmark

Background: It is a commonly accepted belief that cancer cells modify their transcriptional state during the progression of the disease. We propose that the progression of cancer cells towards malignant phenotypes can be efficiently tracked using high-throughput technologies that follow the gradual changes observed in the gene expression profiles by employing Shannon's mathematical theory of communication. Methods based on Information Theory can then quantify the divergence of cancer cells' transcriptional profiles from those of normally appearing cells of the originating tissues. The relevance of the proposed methods can be evaluated using microarray datasets available in the public domain but the method is in principle applicable to other high-throughput methods. Methodology/Principal Findings: Using melanoma and prostate cancer datasets we illustrate how it is possible to employ Shannon Entropy and the Jensen-Shannon divergence to trace the transcriptional changes progression of the disease. We establish how the variations of these two measures correlate with established biomarkers of cancer progression. The Information Theory measures allow us to identify novel biomarkers for both progressive and relatively more sudden transcriptional changes leading to malignant phenotypes. At the same time, the methodology was able to validate a large number of genes and processes that seem to be implicated in the progression of melanoma and prostate cancer. Conclusions/Significance: We thus present a quantitative guiding rule, a new unifying hallmark of cancer: the cancer cell's transcriptome changes lead to measurable observed transitions of Normalized Shannon Entropy values (as measured by high-throughput technologies). At the same time, tumor cells increment their divergence from the normal tissue profile increasing their disorder via creation of states that we might not directly measure. This unifying hallmark allows, via the the Jensen-Shannon divergence, to identify the arrow of time of the processes from the gene expression profiles, and helps to map the phenotypical and molecular hallmarks of specific cancer subtypes. The deep mathematical basis of the approach allows us to suggest that this principle is, hopefully, of general applicability for other diseases

WNT-DEPENDENT REGENERATIVE FUNCTION IS INDUCED IN LEUKEMIA-INITIATING AC133BRIGHT CELLS

The Cancer Stem Cell model supported the notion that leukemia was initiated and maintained in vivo by a small fraction of leukemia-initiating cells (LICs). Previous studies have suggested the involvement of Wnt signaling pathway in Acute Myeloid Leukemia (AML) by the ability to sustain the development of LICs. A novel hematopoietic stem and progenitor cell marker, monoclonal antibody AC133, recognizes the CD34bright CD38- subset of human acute myeloid leukemia cells, suggesting that it may be an early marker for the LICs. During the first part of my phD program we previously evaluated the ability of leukemic AC133+ fraction, to perform engraftment following to xenotransplantation in immunodeficient mouse model Rag2-/-\u3b3c-/-. The results showed that the surface marker AC133 is able to enrich for the cell fraction that contains the LICs. In consideration of our previously reported data, derived from the expression profiling analysis performed in normal (n=10) and leukemic (n=33) human long-term reconstituting AC133+ cells, we revealed that the ligand-dependent Wnt signaling is induced in AML through a diffuse expression and release of WNT10B, a hematopoietic stem cells regenerative-associated molecule. In situ detection performed on bone marrow biopsies of AML patients, showed the activation of the Wnt pathway, through the concomitant presence of the ligand WNT10B and of the active dephosphorylated \u3b2-catenin form, suggesting an autocrine / paracrine-type ligand-dependent activation mechanism. In consideration of the link between hematopoietic regeneration and developmental signaling, we transplanted primary AC133+ AML A46 cells into developing zebrafish. This biosensor model revealed the formation of ectopic structures by activation of dorsal organizer markers that act downstream of the Wnt pathway. These results suggested that the misappropriating Wnt associated functions can promote pathological stem cell-like regeneration responsiveness. The analyses performed in situ retained information on the cellular localization, enabling determination of the activity status of individual cells and allowing the tumor environment view. Taking this issue into consideration, during the second part of my phD program, I set up the application of a new in situ method for localized detection and genotyping of individual transcripts directly in cells and tissues. The mRNA in situ detection technique is based on padlock probes ligation and target priming rolling circle amplification allowing the single nucleotide resolution in heterogenous tissues. The mRNA in situ detection performed on bone marrow biopsies derived from AML patients, showed a diffuse localization pattern of WNT10B molecule in the tissue. Conversely, only the AC133bright cell population shows the Wnt signaling activation signature represented by the cytoplasmatic accumulation and nuclear translocation of the active form of \u3b2-catenin. In spite of this, we previously evidenced that the regenerative function of WNT signaling pathway is defined by the up-regulation of WNT10B, WNT10A, WNT2B and WNT6 loci, we identified the WNT10B as a major locus associated with the regenerative function and over-expressed by all AML patients. By the molecular evaluation of the WNT10B transcript, we isolated an aberrant splicing variant (WNT10BIVS1), that identify Non Core-Binding Factor Leukemia (NCBFL) class and whose potential role is discussed. Moreover, we demonstrate that the function of "leukemia stem cell", present in the cell population enriched for the marker AC133bright, is strictly related to regenerative function associated with WNT signaling, defining the key role of WNT10B ligand as a specific molecular marker for leuchemogenesis. This thesis defines the new suitable approaches to characterize the leukemia-initiating cells (LICs) and suggest the role of WNT10B as a new suitable target for AML

Abstracts of Papers, 89th Annual Meeting of the Virginia Academy of Science, May 25-27, 2011, University of Richmond, Richmond VA

Full abstracts of the 89th Annual Meeting of the Virginia Academy of Science, May 25-27, 2011, University of Richmond, Richmond V

Genomic Approaches to Study Innate Immune Response to Salmonella Enteritidis Infection in Chickens

Salmonella enterica serovar Enteritidis (SE) is one of the most common food-borne pathogens that cause human salmonellosis. Contamination of consumed poultry products continues to be a global threat to public health. Genetic resistance using genomic approach provides a promising solution to controlling SE infection in poultry. The mechanism of SE resistance in chickens remains elusive. Three different approaches, microarray techology, gene silencing, and computational gene analysis, have been utilized to study SE-induced transcriptional changes of host immune response in the chicken. A whole genome chicken 44K microarray was used to analyze the transcriptome of heterophils from SE-resistant (line A) and SE-susceptible chickens (line B) with/without in vitro SE stimulation. Many differentially expressed immune-related genes were found in the SE-infected to non-infected comparison, where more immune-related genes were down-regulated in line B than line A. These results suggested a similar Toll-like receptor (TLR) regulatory network might exist in heterophils of both lines, and provided strong candidates for further investigating SE resistance and susceptibility in chickens. In the gene silencing study, small interfering RNAs (siRNA) were used to specifically inhibit the expression of NFkB1 in the chicken HD11 macrophage cell line with SE challenge. Genes related to the NF-kB signaling pathway were selected to examine the effect of NFkB1 inhibition on TLR pathway. With 36% inhibition of NFkB1 expression, the results showed an increased expression of TLR4 and interleukin (IL)-6 following SE challenge and suggested a likely inhibitory regulation of NFkB1 on TLR signal pathway. Finally, two novel chicken C-type lectin-like receptors were identified and annotated to chicken CD69 and CD94/NKG2-like with multiple evidences generated by computational (in-silico) sequence analysis. Both genes located in a region on chicken chromosome 1 that is syntenic to mammalian Nature Killer Receptor Complex (NKC) region, which may have existed before the divergence between mammals and aves. While siRNA lays the foundation of using loss-of-function approach on testifying gene-gene interactions, in-silico analysis aids in gathering information of unknown genes of great interest. Both approaches provide great potential to use for down-stream analysis following microarray study

2013 IMSAloquium, Student Investigation Showcase

This year, we are proudly celebrating the twenty-fifth anniversary of IMSA’s Student Inquiry and Research (SIR) Program. Our first IMSAloquium, then called Presentation Day, was held in 1989 with only ten presentations; this year we are nearing two hundred.https://digitalcommons.imsa.edu/archives_sir/1005/thumbnail.jp