Dissecting complex transcriptional responses using pathway-level scores based on prior information

<p>Abstract</p> <p>Background</p> <p>The genomewide pattern of changes in mRNA expression measured using DNA microarrays is typically a complex superposition of the response of multiple regulatory pathways to changes in the environment of the cells. The use of prior information, either about the function of the protein encoded by each gene, or about the physical interactions between regulatory factors and the sequences controlling its expression, has emerged as a powerful approach for dissecting complex transcriptional responses.</p> <p>Results</p> <p>We review two different approaches for combining the noisy expression levels of multiple individual genes into robust pathway-level differential expression scores. The first is based on a comparison between the distribution of expression levels of genes within a predefined gene set and those of all other genes in the genome. The second starts from an estimate of the strength of genomewide regulatory network connectivities based on sequence information or direct measurements of protein-DNA interactions, and uses regression analysis to estimate the activity of gene regulatory pathways. The statistical methods used are explained in detail.</p> <p>Conclusion</p> <p>By avoiding the thresholding of individual genes, pathway-level analysis of differential expression based on prior information can be considerably more sensitive to subtle changes in gene expression than gene-level analysis. The methods are technically straightforward and yield results that are easily interpretable, both biologically and statistically.</p

Deciphering Leukaemogenic Mechanisms through System-Scale Analysis of Single-Cell RNA Sequencing Data

Haematopoietic stem cells are responsible for producing and sustaining the diverse array of cell types present in the adult blood system. This complex process requires the strict regulation of haematopoietic fate decisions and differentiation trajectories in order to maintain a healthy state. Haematological malignancies such as leukaemia are associated with various perturbations that disrupt this regulation and drive aberrant cell fate decisions, leading to disease. Much of this dysregulation is proposed to occur at the transcriptional level, and recent technological advancements in single-cell sequencing have made it possible to study the transcriptional effects of leukaemic perturbations at the scale of individual haematopoietic stem and progenitor cells. However, the mechanisms through which specific perturbations lead to dysregulation of the blood system remain poorly understood. The primary aim of this work was to build an integrative computational framework for the analysis and comparison of leukaemic perturbations of the murine blood system as measured by single-cell RNA sequencing. Presented in Chapter 3, this framework aims to dissect the perturbation response across different scales – from individual genes to specific progenitor cell types to the entire blood system – and allow informative comparisons to be made about the similarities and differences between several perturbations. In total, eight genetic perturbations known to associate with leukaemia were analysed, resulting in novel biological insights concerning the behaviour of coordinated gene modules and the cellular abundance shifts driven by them. As many leukaemic drivers act directly upon the most immature long-term haematopoietic stem cells, a highly targeted analysis of these cells was performed across the leukaemic perturbations. In Chapter 4 a novel computational pipeline was built to link FACS-sorted cell populations and single-cell transcriptional landscapes. Using this, the cellular and molecular responses of the perturbations were investigated, resulting in several novel hypotheses. For example, the data suggests that many leukaemic perturbations gain a competitive advantage against wild-type cells by pushing their MPP1 cells into more active states. Additionally the data suggests that increases in the transcriptional variability of blood stem cells is associated with pro-erythroid fate decision shifts and vice-versa. Many different types of haematopoietic perturbations exist and can drive disease progression in the blood system. Chapter 5 focuses on single-cell RNA sequencing data from three further perturbations in various settings, including an infection model of Malaria and a model susceptible to endogenous DNA damage by aldehydes. These analyses have driven and validated bodies of experimental work, and comparing them to the previously described perturbation models highlighted both conserved changes and differences in the response of the haematopoietic system across different perturbation settings. The final project aimed to improve upon current computational methods for cellular trajectory inference from single-cell data. Whilst high-throughput experiments allow for the sequencing of large cell numbers, this is balanced by the sparse and noisy nature of the returned data. Current methods perform poorly on such datasets and either cannot deal with large cell numbers or cannot extract enough relevant signal from sparse count matrices. A new computational tool was designed to work best on these large, sparse datasets, and infer the most likely cellular trajectories through snapshot sequencing data using an iterative process. In Chapter 6 this algorithm was applied to different systems including adult haematopoiesis, and was compared to state-of-the-art methods. Overall, this thesis has investigated the transcriptional consequences of numerous preleukaemic perturbations on the haematopoietic stem and progenitor cell compartment at the single-cell level. New methods have been built for integration of single-cell perturbation experiments and their analysis across different biological scales. This has revealed novel biological insights regarding the mechanisms underpinning leukaemic transformation of the blood system

Spectral analysis of gene expression profiles using gene networks

Microarrays have become extremely useful for analysing genetic phenomena, but establishing a relation between microarray analysis results (typically a list of genes) and their biological significance is often difficult. Currently, the standard approach is to map a posteriori the results onto gene networks to elucidate the functions perturbed at the level of pathways. However, integrating a priori knowledge of the gene networks could help in the statistical analysis of gene expression data and in their biological interpretation. Here we propose a method to integrate a priori the knowledge of a gene network in the analysis of gene expression data. The approach is based on the spectral decomposition of gene expression profiles with respect to the eigenfunctions of the graph, resulting in an attenuation of the high-frequency components of the expression profiles with respect to the topology of the graph. We show how to derive unsupervised and supervised classification algorithms of expression profiles, resulting in classifiers with biological relevance. We applied the method to the analysis of a set of expression profiles from irradiated and non-irradiated yeast strains. It performed at least as well as the usual classification but provides much more biologically relevant results and allows a direct biological interpretation

Mapping genetic interactions in cancer: a road to rational combination therapies.

The discovery of synthetic lethal interactions between poly (ADP-ribose) polymerase (PARP) inhibitors and BRCA genes, which are involved in homologous recombination, led to the approval of PARP inhibition as a monotherapy for patients with BRCA1/2-mutated breast or ovarian cancer. Studies following the initial observation of synthetic lethality demonstrated that the reach of PARP inhibitors is well beyond just BRCA1/2 mutants. Insights into the mechanisms of action of anticancer drugs are fundamental for the development of targeted monotherapies or rational combination treatments that will synergize to promote cancer cell death and overcome mechanisms of resistance. The development of targeted therapeutic agents is premised on mapping the physical and functional dependencies of mutated genes in cancer. An important part of this effort is the systematic screening of genetic interactions in a variety of cancer types. Until recently, genetic-interaction screens have relied either on the pairwise perturbations of two genes or on the perturbation of genes of interest combined with inhibition by commonly used anticancer drugs. Here, we summarize recent advances in mapping genetic interactions using targeted, genome-wide, and high-throughput genetic screens, and we discuss the therapeutic insights obtained through such screens. We further focus on factors that should be considered in order to develop a robust analysis pipeline. Finally, we discuss the integration of functional interaction data with orthogonal methods and suggest that such approaches will increase the reach of genetic-interaction screens for the development of rational combination therapies

Multi-scale molecular descriptions of human heart failure using single cell, spatial, and bulk transcriptomics

Molecular descriptions of human disease have relied on transcriptomics, the genome-wide measurement of gene expression. In the last years the emergence of capture-based technologies have enabled the transcriptomic profiling of single cells both from dissociated and intact tissues, providing a spatial and cell type specific context that complements the catalog of gene expression changes reported from bulk technologies. In the context of cardiovascular disease, these technologies open the opportunity to study the inter and intra-cellular mechanisms that regulate myocardial remodeling. In this thesis I present comprehensive descriptions of the transcriptional changes in acute and chronic human heart failure using bulk, single cell, and spatial technologies. First, I describe the creation of the Reference of the Heart Failure Transcriptome, a resource built from the meta-analysis of 16 independent studies of human heart failure transcriptomics. Then, I report the first spatial and single cell atlas of human myocardial infarction, and propose a computational strategy to identify compositional, organizational, and molecular tissue differences across distinct time points and physiological zones of damaged myocardium. Finally, I outline a methodology for the multicellular analysis of single cell data that allows for a better understanding of tissue responses and cell type coordination events in cardiovascular disease and that links the knowledge of independent studies at multiple scales. Overall my work demonstrates the importance of the generation of reliable molecular references of disease across scales

Genetic Dissection of Acute Ethanol Responsive Gene Networks in Prefrontal Cortex: Functional and Mechanistic Implications

Background Individual differences in initial sensitivity to ethanol are strongly related to the heritable risk of alcoholism in humans. To elucidate key molecular networks that modulate ethanol sensitivity we performed the first systems genetics analysis of ethanol-responsive gene expression in brain regions of the mesocorticolimbic reward circuit (prefrontal cortex, nucleus accumbens, and ventral midbrain) across a highly diverse family of 27 isogenic mouse strains (BXD panel) before and after treatment with ethanol. Results Acute ethanol altered the expression of ~2,750 genes in one or more regions and 400 transcripts were jointly modulated in all three. Ethanol-responsive gene networks were extracted with a powerful graph theoretical method that efficiently summarized ethanol\u27s effects. These networks correlated with acute behavioral responses to ethanol and other drugs of abuse. As predicted, networks were heavily populated by genes controlling synaptic transmission and neuroplasticity. Several of the most densely interconnected network hubs, including Kcnma1 and Gsk3β, are known to influence behavioral or physiological responses to ethanol, validating our overall approach. Other major hub genes like Grm3, Pten and Nrg3 represent novel targets of ethanol effects. Networks were under strong genetic control by variants that we mapped to a small number of chromosomal loci. Using a novel combination of genetic, bioinformatic and network-based approaches, we identified high priority cis-regulatory candidate genes, including Scn1b,Gria1, Sncb and Nell2. Conclusions The ethanol-responsive gene networks identified here represent a previously uncharacterized intermediate phenotype between DNA variation and ethanol sensitivity in mice. Networks involved in synaptic transmission were strongly regulated by ethanol and could contribute to behavioral plasticity seen with chronic ethanol. Our novel finding that hub genes and a small number of loci exert major influence over the ethanol response of gene networks could have important implications for future studies regarding the mechanisms and treatment of alcohol use disorders

Transcriptome and translatome profiles of Streptomyces species in different growth phases.

Streptomyces are efficient producers of various bioactive compounds, which are mostly synthesized by their secondary metabolite biosynthetic gene clusters (smBGCs). The smBGCs are tightly controlled by complex regulatory systems at transcriptional and translational levels to effectively utilize precursors that are supplied by primary metabolism. Thus, dynamic changes in gene expression in response to cellular status at both the transcriptional and translational levels should be elucidated to directly reflect protein levels, rapid downstream responses, and cellular energy costs. In this study, RNA-Seq and ribosome profiling were performed for five industrially important Streptomyces species at different growth phases, for the deep sequencing of total mRNA, and only those mRNA fragments that are protected by translating ribosomes, respectively. Herein, 12.0 to 763.8 million raw reads were sufficiently obtained with high quality of more than 80% for the Phred score Q30 and high reproducibility. These data provide a comprehensive understanding of the transcriptional and translational landscape across the Streptomyces species and contribute to facilitating the rational engineering of secondary metabolite production

Neoadjuvant anti-PD-1 immunotherapy promotes a survival benefit with intratumoral and systemic immune responses in recurrent glioblastoma.

Glioblastoma is the most common primary malignant brain tumor in adults and is associated with poor survival. The Ivy Foundation Early Phase Clinical Trials Consortium conducted a randomized, multi-institution clinical trial to evaluate immune responses and survival following neoadjuvant and/or adjuvant therapy with pembrolizumab in 35 patients with recurrent, surgically resectable glioblastoma. Patients who were randomized to receive neoadjuvant pembrolizumab, with continued adjuvant therapy following surgery, had significantly extended overall survival compared to patients that were randomized to receive adjuvant, post-surgical programmed cell death protein 1 (PD-1) blockade alone. Neoadjuvant PD-1 blockade was associated with upregulation of T cell- and interferon-γ-related gene expression, but downregulation of cell-cycle-related gene expression within the tumor, which was not seen in patients that received adjuvant therapy alone. Focal induction of programmed death-ligand 1 in the tumor microenvironment, enhanced clonal expansion of T cells, decreased PD-1 expression on peripheral blood T cells and a decreasing monocytic population was observed more frequently in the neoadjuvant group than in patients treated only in the adjuvant setting. These findings suggest that the neoadjuvant administration of PD-1 blockade enhances both the local and systemic antitumor immune response and may represent a more efficacious approach to the treatment of this uniformly lethal brain tumor