Interactions between platelets and hematopoietic cells

In addition to their primary role in hemostasis, platelets are increasingly recognized as important participants in numerous biological processes. Their ability to adhere to and communicate with different immune cells, endothelial cells, and cancer cells makes them a natural nexus that participates in development of different diseases, including cancer. Thus, one could also surmise interactions between platelets and hematopoietic stem and progenitor cells. Previous studies have shown that bone marrow function recovers more quickly after transplantation with mobilized peripheral blood stem cells than with bone marrow-derived hematopoietic stem cells. A major difference between the two techniques is that mobilized peripheral blood stem cells are exposed to activated platelets during harvesting. As platelets communicate with a myriad of blood cells and carry cargoes of hundreds of proteins and other biologically active compounds, I wanted to investigate potential interactions between platelets and hematopoietic progenitor cells, including leukemic cells from acute myelogenous leukemia (AML). Using flow cytometric analysis and colony forming unit (CFU) assessment, our group show that platelet releasate inhibits proliferation, conserves erythroid phenotype, and increases levels of erythroid progenitors in cultivated mobilized peripheral blood stem and progenitor cells. Expression of CD14 antigen and monocyte-associated mRNAs also increased, suggesting that platelet releasate induced monocytopoiesis. Upon activation, platelets degranulate and release the content of their alpha granules, dense granules, and lysosomes. Activated platelets also shed platelet microparticles (PMP), membranous vesicles that contain platelet cargo. These microparticles are internalized by many different cells, including cancer cells, and are known to alter their biological behavior. Using flow cytometry and fluorescence microscopy, we show that these microparticles are internalized by AML cells, with a subsequent transfer of miR-125a and miR-125b and a downregulation of the pro-apoptotic protein PUMA. This microRNA transfer could explain the anti-apoptotic properties of PMPs that we also observed following treatment with several apoptosis inductors, where daunorubicin is of particular interest, as it is a mainstay in the treatment of AML. Thus, multiple potential interactions between platelets and hematopoietic progenitor cells and leukemic cells are identified. The results must be confirmed by more advanced in vitro and translational models before their clinical relevance can be fully appreciated, but the findings may benefit ex vivo production of monocytes and erythrocytes and support the use of therapeutic platelet inhibition in AML patients

Identifying the molecular components that matter: a statistical modelling approach to linking functional genomics data to cell physiology

Functional genomics technologies, in which thousands of mRNAs, proteins, or metabolites can be measured in single experiments, have contributed to reshape biological investigations. One of the most important issues in the analysis of the generated large datasets is the selection of relatively small sub-sets of variables that are predictive of the physiological state of a cell or tissue. In this thesis, a truly multivariate variable selection framework using diverse functional genomics data has been developed, characterized, and tested. This framework has also been used to prove that it is possible to predict the physiological state of the tumour from the molecular state of adjacent normal cells. This allows us to identify novel genes involved in cell to cell communication. Then, using a network inference technique networks representing cell-cell communication in prostate cancer have been inferred. The analysis of these networks has revealed interesting properties that suggests a crucial role of directional signals in controlling the interplay between normal and tumour cell to cell communication. Experimental verification performed in our laboratory has provided evidence that one of the identified genes could be a novel tumour suppressor gene. In conclusion, the findings and methods reported in this thesis have contributed to further understanding of cell to cell interaction and multivariate variable selection not only by applying and extending previous work, but also by proposing novel approaches that can be applied to any functional genomics data

Novel methods based on regression techniques to analyze multistate models and high-dimensional omics data.

The dissertation is based on four distinct research projects that are loosely interconnected by the common link of a regression framework. Chapter 1 provides an introductory outline of the problems addressed in the projects along with a detailed review of the previous works that have been done on them and a brief discussion on our newly developed methodologies. Chapter 2 describes the first project that is concerned with the identification of hidden subject-specific sources of heterogeneity in gene expression profiling analyses and adjusting for them by a technique based on Partial Least Squares (PLS) regression, in order to ensure a more accurate inference on the expression pattern of the genes over two different varieties of samples. Chapter 3 focuses on the development of an R package based on Project 1 and its performance evaluation with respect to other popular software dealing with differential gene expression analyses. Chapter 4 covers the third project that proposes a non-parametric regression method for the estimation of stage occupation probabilities at different time points in a right-censored multistate model data, using an Inverse Probability of Censoring (IPCW) (Datta and Satten, 2001) based version of the backfitting principle (Hastie and Tibshirani, 1992). Chapter 5 describes the fourth project which deals with the testing for the equality of the residual distributions after adjusting for available covariate information from the right censored waiting times of two groups of subjects, by using an Inverse Probability of Censoring weighted (IPCW) version of the Mann-Whitney U test

Computational Methods for the Analysis of Genomic Data and Biological Processes

In recent decades, new technologies have made remarkable progress in helping to understand biological systems. Rapid advances in genomic profiling techniques such as microarrays or high-performance sequencing have brought new opportunities and challenges in the fields of computational biology and bioinformatics. Such genetic sequencing techniques allow large amounts of data to be produced, whose analysis and cross-integration could provide a complete view of organisms. As a result, it is necessary to develop new techniques and algorithms that carry out an analysis of these data with reliability and efficiency. This Special Issue collected the latest advances in the field of computational methods for the analysis of gene expression data, and, in particular, the modeling of biological processes. Here we present eleven works selected to be published in this Special Issue due to their interest, quality, and originality

Genetic algorithm-neural network: feature extraction for bioinformatics data.

With the advance of gene expression data in the bioinformatics field, the questions which frequently arise, for both computer and medical scientists, are which genes are significantly involved in discriminating cancer classes and which genes are significant with respect to a specific cancer pathology. Numerous computational analysis models have been developed to identify informative genes from the microarray data, however, the integrity of the reported genes is still uncertain. This is mainly due to the misconception of the objectives of microarray study. Furthermore, the application of various preprocessing techniques in the microarray data has jeopardised the quality of the microarray data. As a result, the integrity of the findings has been compromised by the improper use of techniques and the ill-conceived objectives of the study. This research proposes an innovative hybridised model based on genetic algorithms (GAs) and artificial neural networks (ANNs), to extract the highly differentially expressed genes for a specific cancer pathology. The proposed method can efficiently extract the informative genes from the original data set and this has reduced the gene variability errors incurred by the preprocessing techniques. The novelty of the research comes from two perspectives. Firstly, the research emphasises on extracting informative features from a high dimensional and highly complex data set, rather than to improve classification results. Secondly, the use of ANN to compute the fitness function of GA which is rare in the context of feature extraction. Two benchmark microarray data have been taken to research the prominent genes expressed in the tumour development and the results show that the genes respond to different stages of tumourigenesis (i.e. different fitness precision levels) which may be useful for early malignancy detection. The extraction ability of the proposed model is validated based on the expected results in the synthetic data sets. In addition, two bioassay data have been used to examine the efficiency of the proposed model to extract significant features from the large, imbalanced and multiple data representation bioassay data

Can Systems Biology Advance Clinical Precision Oncology?

Precision oncology is perceived as a way forward to treat individual cancer patients. However, knowing particular cancer mutations is not enough for optimal therapeutic treatment, because cancer genotype-phenotype relationships are nonlinear and dynamic. Systems biology studies the biological processes at the systems’ level, using an array of techniques, ranging from statistical methods to network reconstruction and analysis, to mathematical modeling. Its goal is to reconstruct the complex and often counterintuitive dynamic behavior of biological systems and quantitatively predict their responses to environmental perturbations. In this paper, we review the impact of systems biology on precision oncology. We show examples of how the analysis of signal transduction networks allows to dissect resistance to targeted therapies and inform the choice of combinations of targeted drugs based on tumor molecular alterations. Patient-specific biomarkers based on dynamical models of signaling networks can have a greater prognostic value than conventional biomarkers. These examples support systems biology models as valuable tools to advance clinical and translational oncological research