Comparative Analysis of Small Non-Coding RNA and Messenger RNA Expression in Somatic Cell Nuclear Transfer and In Vitro-Fertilized Bovine Embryos During Early Development Through the Maternal-to-Embryonic Transition

Cloning animals using somatic cell nuclear transfer (scNT) was first successfully demonstrated with the birth of Dolly the sheep, but the process of cloning remains highly inefficient. By improving our understanding of the errors that may occur during cloned cattle embryo development, we could obtain a greater understanding of how specific molecular events contribute to successful development. The central dogma of biology refers to the process of DNA being transcribed into messenger RNA (mRNA) and the translation of mRNA into proteins, which ultimately carry out the functions encoded by genes. The epigenetic code is defined as the array of chemical modifications, or “marks”, to DNA molecules that do not change the genome sequence but do allow for control of gene expression. During early development, genome reprogramming involves the removal of epigenetic marks from the sperm and egg and re-establishment of marks for the embryonic genome that code for proper gene expression to support embryo development. The point during this process at which the embryo’s genes are turned on is known as embryonic genome activation (EGA). Small non-coding RNAs (sncRNAs), including microRNAs (miRNAs), may also contribute to the this process. For example, miRNA molecules do not code for proteins themselves, but rather bind to mRNAs and effectively block their translation into protein. We hypothesized that aberrant expression of sncRNAs in cloned embryos may lead to anomalous abundance of mRNA molecules, thus explaining poor development of cloned embryos. First, we used RNA sequencing to examine the total population of sncRNAs in cattle embryos produced by in vitro fertilization (IVF) and found a dramatic shift in populations at the EGA. Next, we collected both sncRNA and mRNA from scNT cattle embryos, and again performed sequencing of both RNA fractions. We found that few sncRNAs were abnormally expressed in scNT embryos, with all differences appearing after EGA at the morula developmental stage. However, notable differences in the populations of sncRNAs were evident when comparing embryos by developmental stage. For populations of mRNA, we observed dramatic differences when comparing scNT and IVF cattle embryos, with the highest number of changes occurring at the EGA (8-cell stage) and after (morula stage). While changes in specific miRNA molecules (miR-34a and miR-345) were negatively correlated with some of their predicted target mRNAs, this pattern was not widespread as would be expected if these sncRNAs are functionally binding to all of the predicted mRNA targets. Collectively, our observations suggest that other mechanisms leading to altered expression of mRNA in cloned embryos may be responsible for their relatively poor development

Systems Analytics and Integration of Big Omics Data

A “genotype"" is essentially an organism's full hereditary information which is obtained from its parents. A ""phenotype"" is an organism's actual observed physical and behavioral properties. These may include traits such as morphology, size, height, eye color, metabolism, etc. One of the pressing challenges in computational and systems biology is genotype-to-phenotype prediction. This is challenging given the amount of data generated by modern Omics technologies. This “Big Data” is so large and complex that traditional data processing applications are not up to the task. Challenges arise in collection, analysis, mining, sharing, transfer, visualization, archiving, and integration of these data. In this Special Issue, there is a focus on the systems-level analysis of Omics data, recent developments in gene ontology annotation, and advances in biological pathways and network biology. The integration of Omics data with clinical and biomedical data using machine learning is explored. This Special Issue covers new methodologies in the context of gene–environment interactions, tissue-specific gene expression, and how external factors or host genetics impact the microbiome

The HBP1 tumor suppressor is a negative epigenetic regulator of MYCN driven neuroblastoma through interaction with the PRC2 complex

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Abstracts from the 50th European Society of Human Genetics Conference: Posters

International audienc

Computational Cancer Research: Network-based analysis of cancer data disentangles clinically relevant alterations from molecular measurements

Cancer is a very complex genetic disease driven by combinations of mutated genes. This complexity strongly complicates the identification of driver genes and puts enormous challenges to reveal how they influence cancerogenesis, prognosis or therapy response. Thousands of molecular profiles of the major human types of cancer have been measured over the last years. Apart from well-studied frequently mutated genes, still only little is known about the role of rarely mutated genes in cancer or the interplay of mutated genes in individual cancers. Gene expression and mutation profiles can be measured routinely, but computational methods for the identification of driver candidates along with the prediction of their potential impacts on downstream targets and clinically relevant characteristics only rarely exist. Instead of only focusing on frequently mutated genes, each cancer patient should better be analyzed by using the full information in its cancer-specific molecular profiles to improve the understanding of cancerogenesis and to more precisely predict prognosis and therapy response of individual patients. This requires novel computational methods for the integrative analysis of molecular cancer data. A promising way to realize this is to consider cancer as a disease of cellular networks. Therefore, I have developed a novel network-based approach for the integrative analysis of molecular cancer data over the last years. This approach directly learns gene regulatory networks form gene expression and copy number data and further enables to quantify impacts of altered genes on clinically relevant downstream targets using network propagation. This habilitation thesis summarizes the results of seven of my publications. All publications have a focus on the integrative analysis of molecular cancer data with an overarching connection to the newly developed network-based approach. In the first three publications, networks were learned to identify major regulators that distinguish characteristic gene expression signatures with applications to astrocytomas, oligodendrogliomas, and acute myeloid leukemia. Next, the central publication of this habilitation thesis, which combines network inference with network propagation, is introduced. The great value of this approach is demonstrated by quantifying potential direct and indirect impacts of rare and frequent gene copy number alterations on patient survival. Further, the publication of the corresponding user-friendly R package regNet is introduced. Finally, two additional publications that also strongly highlight the value of the developed network-based approach are presented with the aims to predict cancer gene candidates within the region of the 1p/19q co-deletion of oligodendrogliomas and to determine driver candidates associated with radioresistance and relapse of prostate cancer. All seven publications are embedded into a brief introduction that motivates the scientific background and the major objectives of this thesis. The background is briefly going from the hallmarks of cancer over the complexity of cancer genomes down to the importance of networks in cancer. This includes a short introduction of the mathematical concepts that underlie the developed network inference and network propagation algorithms. Further, I briefly motivate and summarize my studies before the original publications are presented. The habilitation thesis is completed with a general discussion of the major results with a specific focus on the utilized network-based data analysis strategies. Major biologically and clinically relevant findings of each publication are also briefly summarized

Systems biology approaches to precision medicine

This dissertation reviews the development and implementation of two systems biology meth- ods: ADVOCATE and hpARACNE. ADVOCATE was designed to deconvolve epithelium and stroma compartments fractions and virtual expression profiles from bulk gene expression profiles from human patients. We used laser capture microdissection and RNA sequencing to disentangle the transcriptional programs active in the malignant epithelium and stroma of pancreatic ductal adenocarcinoma (PDA), an aggressive malignancy with a prominent stromal component. We learned that distinct molecular subtypes are present in both the epithelium and the stroma of pancreatic cancer, and that the subtype identity of these two compartments are independent of one another. Critically, we discovered that specific com- binations of epithelial and stromal subtypes are strongly associated with patient survival across multiple external datasets, exhibiting both an effect-size and a level of reproducibility that was absent from previous efforts. These analyses were made possible by a new proba- bilistic algorithm (Adaptive DeconVolution Of CAncer Tissue Expression - ADVOCATE) that can extract compartment-specific gene expression profiles from bulk gene expression data. ADVOCATE accurately predicted the compartment fractions of bulk tumor samples and improved the performance of molecular classifiers by controlling for the diverse cellular compositions of independent datasets. This approach provides a much-needed framework to handle solid tumor tissue heterogeneity, allowing integrated analysis of both epithelial and stromal transcriptional programs from individual bulk samples. Reverse engineering approaches have been used to systematically dissect regulatory in- teractions based on gene expression profiles in different context and data types, thus im- proving our mechanistic understanding of molecular programs under perturbations. Pro- teomics data, on the other hand, provides direct evidence of cell functions. Particularly, signaling molecules are best candidates for drug targets. Previous efforts have shown that targeting signaling proteins could potentially lead to cancer remission. In this work, I introduce hybrid proteomics Algorithm for the Reconstruction of Accurate Cellular Network (hpARACNE), a re-design of gene expression based ARACNE algorithm. Us- ing Clinical Proteomics Tumor Analysis Consortium (CPTAC) breast cancer proteomics data, hpARACNE reconstructs a network that significantly outperforms ARACNE when compared with curated Kinase/Phosphatase-substrates interactions from public databases. Compared with Stable Isotope Labeling with Amino acid in Cell Culture (SILAC) ex- perimentally identified substrates for EGFR, hpARACNE predicts substrates with high accuracy. Integrative network analysis of breast cancer transcriptome and phosphopro- teome reveals potential drug targets for Triple Negative Breast Cancer (TNBC) treat- ment. hpARACNE has three innovations that adapt it to proteomics data and signaling process: 1) Refinement of the kinase/phosphatase peptides by integrating matched whole proteomic and whole phosphoproteomic profiles; 2) Establishment of association based on newly designed Mutual Information (MI) estimator for missing data; 3) Network pruning using directional Data Processing Inequality (dDPI) for signalling process

Grand Celebration: 10th Anniversary of the Human Genome Project

In 1990, scientists began working together on one of the largest biological research projects ever proposed. The project proposed to sequence the three billion nucleotides in the human genome. The Human Genome Project took 13 years and was completed in April 2003, at a cost of approximately three billion dollars. It was a major scientific achievement that forever changed the understanding of our own nature. The sequencing of the human genome was in many ways a triumph for technology as much as it was for science. From the Human Genome Project, powerful technologies have been developed (e.g., microarrays and next generation sequencing) and new branches of science have emerged (e.g., functional genomics and pharmacogenomics), paving new ways for advancing genomic research and medical applications of genomics in the 21st century. The investigations have provided new tests and drug targets, as well as insights into the basis of human development and diagnosis/treatment of cancer and several mysterious humans diseases. This genomic revolution is prompting a new era in medicine, which brings both challenges and opportunities. Parallel to the promising advances over the last decade, the study of the human genome has also revealed how complicated human biology is, and how much remains to be understood. The legacy of the understanding of our genome has just begun. To celebrate the 10th anniversary of the essential completion of the Human Genome Project, in April 2013 Genes launched this Special Issue, which highlights the recent scientific breakthroughs in human genomics, with a collection of papers written by authors who are leading experts in the field